Titanic EDA .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f317590",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6afd35a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff46e44",
   "metadata": {},
   "source": [
    "## Importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e82387ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0a4a91a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bac4cd4",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd99246",
   "metadata": {},
   "source": [
    "Let's check if there are null values  in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5a2cfc9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e319269e",
   "metadata": {},
   "source": [
    "Let's visually find out the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "33f9a4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Heatmap showing null values in dataset')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna(), yticklabels = False  )\n",
    "plt.title(\"Heatmap showing null values in dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5070eb78",
   "metadata": {},
   "source": [
    "Only age and cabin mobile has null values in which age column has about 15% null values. Cabin has more than 70% null values and it is categorical data. So, in this case, we can convert this column to contain labels 1 and 0, where 0 means unknown cabin, and 1 means known cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07a4238",
   "metadata": {},
   "source": [
    "### Lets fill missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf3c501",
   "metadata": {},
   "source": [
    "lets fill the missing values in cabin column in the way mentioned before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a37d1fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_missing(col):\n",
    "    if pd.isnull(col):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8d5740e9",
   "metadata": {},
   "outputs": [],
   "source": [
    " df['Cabin'] = df['Cabin'].apply(fill_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c29af56c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    687\n",
       "1    204\n",
       "Name: Cabin, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7141e9",
   "metadata": {},
   "source": [
    "To fill missing values in age column. Let's first find relationship between the columns to fill the appropriate values in age column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4010bcf5",
   "metadata": {},
   "source": [
    "## Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "370b27e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare  Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500      0        S  \n",
       "1      0          PC 17599  71.2833      1        C  \n",
       "2      0  STON/O2. 3101282   7.9250      0        S  \n",
       "3      0            113803  53.1000      1        S  \n",
       "4      0            373450   8.0500      0        S  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c6059b",
   "metadata": {},
   "source": [
    "Lets first find the number of survived people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "59820313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1ebe12ab100>,\n",
       "  <matplotlib.axis.XTick at 0x1ebe12ab0d0>],\n",
       " [Text(0, 0, 'Dead'), Text(1, 0, 'Alive')])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "g = sns.countplot(data =df, x = 'Survived')\n",
    "plt.title('Count of Survived people')\n",
    "plt.xticks([0,1],['Dead' , 'Alive'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef33befa",
   "metadata": {},
   "source": [
    "Let's find the relationship between survival and sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2b82a942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of Survived people based upon sex')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "g = sns.countplot(data =df, x = 'Survived' , hue = 'Sex')\n",
    "plt.title('Count of Survived people based upon sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24704d95",
   "metadata": {},
   "source": [
    "Majority of female survived"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd46ed9",
   "metadata": {},
   "source": [
    "Let's find the relationship between Survival and Passenger Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8888cdad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relation between survival and passenger class')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x = 'Survived' , hue = 'Pclass')\n",
    "plt.title('Relation between survival and passenger class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2440acd",
   "metadata": {},
   "source": [
    "Majority of 1st class passengers survived"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b768c77b",
   "metadata": {},
   "source": [
    "Let's find the Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f644b2f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age Distribution')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = df['Age'].dropna() , kde = True)\n",
    "plt.title(\"Age Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70149f6",
   "metadata": {},
   "source": [
    "Age is uniformly distributed with mean of around 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c6e99ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data = df, x = 'Pclass' , y = 'Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a3a0b5",
   "metadata": {},
   "source": [
    "Different passenger class has different mean. So, let's check the outliers first and then, we will fill the missing values in age columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8db0d8be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TXl6eJkyYEOqYACyi42JfXee3SGeO1HKxL5jABejMMnJyOC4u7qxtVTozqXbDhg0hTgTAqjou9rV3795uLxROp1M33HADF/tCyDEmzeKS/wAsjYt9wWoYk2ZRXABYHjdZhNV0jMmOkuJwOBiTIUJxAWALmZmZ3T7/2c9+ZigJcMaMGTM0ZMgQSdLQoUPP+pZ99D2KCwBbePbZZ7t9vnjxYkNJgDNcLpfmz5+vuLg4zZs3j4vPhQhXbgJgebt37+5xRc2amhrt3r1bY8aMMZQKkFJTU5Wammo6RljhiAsAS/P7/Xr66afPuu7pp5/u8TZpAP0bxQWApW3fvl3Hjh0767pjx45p+/btIU4EwCSKCwBLS0lJ0WWXXXbWdZdffrlSUlJCnAj4s8rKSmVnZ6uystJ0lLBBcQFgaREREec8VfTMM88oIoJfYzDD6/WqsLBQjY2NKiws7HGTYFwc/MQDsLwxY8bI4/F0W5aUlKTRo0cbSgRIRUVFampqkiQ1NTWpuLjYcKLwQHEBYAv5+fmdR1ciIiL03HPPGU6EcMbdoc2huACwhdjYWM2YMUMRERGaMWOGYmNjTUdCmOLu0GZxHRcAvRIIBIyfw58+fbqmT58uh8Oh1tZWYzlcLhf3owljHXeH/q6ud4e++uqrQx8sTFBcAHyvQCCg3Nxcff7556ajWILH49GqVasoL2GKu0ObxakiAL3CizRwBneHNosjLgC+l8Ph0KpVq4yeKvJ6vZo0aZIkafPmzUbvC8OpInTcHfqNN95QIBDg7tAhRHEB0CsOh0Nut9t0DElnioNVsiB8zZgxQ++++64OHz7M3aFDiFNFAACcB+4ObQZHXAAAOE/cHTr0OOICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg0v+AwBsKRAIGL1jeUcGScbvFh5OdyynuAAAbCcQCCg3N1eff/656SiW4PF4tGrVqrAoL5wqAgDYUji8SKMnI0dc3n33XT3yyCMaOHBg57LbbrtNy5cvV3V1tZ577jnV19dr0KBBeuCBB5SdnW0iJgDAohwOh1atWmX0VJHX69WkSZMkSZs3b5bL5TKWhVNFF1ltba0mTZqkgoKCbsubm5s1e/ZszZkzR9OmTVNVVZVyc3M1cuRIJSUlmYgKALAoh8Mht9ttOoakM8XBKln6OyOnimprazVq1KgeyysqKhQbG6ucnBxFRkYqJSVFmZmZKi4uNpASAABYTciPuPj9fn3xxRdyu91as2aNfD6fJk6cqEceeUR1dXVKTEzs9vj4+HiVlpYGvR2fz9dXkcNO133n8/nYl7AExiWshjHZt3q7/0JeXI4cOaK//du/VUZGhlauXKlvv/1Wjz76qBYsWKArrriix6E2l8ullpaWoLdTW1vbV5HDTltbW+fHNTU13eYiAaYwLmE1jEkzQl5chg4d2u3Uj9vt1oIFC3TXXXdpypQpPSZaeb1eRUdHB70dj8cjp9N5wXnDUWtra+fHSUlJnLeFJTAuYTWMyb7l8/l6ddAh5MXlyy+/1Ntvv6358+d3zoA+deqUIiIilJSUpNdff73b4+vr65WQkBD0dpxOZ9DFxQoXM7KC9vb2bh9TAM8Ip1n7VtR1HJ7PzzfQ1xiTZoS8uMTGxqq4uFiXX365Zs2apYMHD2r58uX6+7//e2VkZKiwsFDr169XTk6O9uzZo7KyMq1evTok2bxerzIyMkKyLbvoeKsfpK1bt/IXFQAYFvJ3FQ0bNkyvvvqqPvjgA40bN05ZWVnyeDx66qmnNGjQIK1bt07l5eUaP3688vLylJeXpwkTJoQ6JgAAsCAj13EZN26cNmzYcNZ1Ho/nnOtC6eToHCkijO+I8Kf7byjcT434Tyt6L2/HBwCrCONX5u8RESk5B5hOAQAAuuBeRQAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDYoLgAAwDbOu7gcOXKkL3MAAAB8r6CKy+nTp7VixQrdcMMNSktL0/79+5WVlaWDBw9erHwAAACdgiouL730knbs2KEXX3xRAwYM0JAhQzRs2DDl5+dfrHwAAACdIoN5cFlZmd566y3FxcXJ4XDokksuUUFBgdLT0y9WPgAAgE5BHXFpaWnR4MGDJUmBQECS5HK5FBHBHF8AAHDxBdU4fvzjH2vVqlWSJIfDIUl644035PF4+j4ZAADAdwR1qmjRokW65557tGnTJp08eVJ33HGHTp48qd/85jcXKx8AAECnoIrLlVdeqXfeeUcffvihGhoaNGzYMN1yyy2KiYm5WPkAAAA6BXWqqKGhQd9++61+/OMf64477tDo0aN17NgxHTp0SKdOnQp64z6fTzNnztRjjz3Wuay6ulrZ2dlKTk5WWlqaSkpKgn5eAADQPwV1xCU9PV1+v/+s6yIiInTjjTdq6dKlnRN4v8+qVau0e/duDR8+XJLU3Nys2bNna86cOZo2bZqqqqqUm5urkSNHKikpKZioAACgHwrqiMvjjz+uG2+8UW+//baqq6v1zjvvaOLEicrNzdWmTZsUExOjgoKCXj3X9u3bVVFRodtvv71zWUVFhWJjY5WTk6PIyEilpKQoMzNTxcXFwf2rAABAvxTUEZfXX39dJSUlio2NlSRde+21Wrp0qbKysvTQQw9p8eLFuvXWW7/3eZqamrRo0SKtXr1a69ev71xeV1enxMTEbo+Nj49XaWlpMDElnTkNFYqvQfjw+XyMEYO67nu+F7ACxmTf6u3+C6q4fPvtt3I6nd2WORwONTU1SZLcbvc5TyV18Pv9WrBggWbNmqXrrruu27qTJ0/K7XZ3W+ZyudTS0hJMTElSbW1t0F/T1tYW9NcgfNTU1GjgwIGmY4Strj+ffC9gBYxJM4IqLjfffLPmz5+vRYsW6Yc//KEaGhq0bNkypaam6tSpU3r55Zd1/fXX/8XnePXVVxUVFaWZM2f2WOd2u3X8+PFuy7xer6Kjo4OJKUnyeDw9Stb3aW1tDXo7CB9JSUk9ijVCp+vPJ98LWAFjsm/5fL5eHXQIqrj88pe/1Pz585WRkdF5AbpbbrlF+fn52r17tz766CP96le/+ovPsXnzZh08eFBjxoyRdKaYSNJ//ud/auHChaqsrOz2+Pr6eiUkJAQTU5LkdDqDLi7BPh7h5XzGFPpO133P9wJWwJg0I6jiEhsbq7Vr16qxsVH/93//p0AgoI0bNyotLU2fffaZNm/e/L3PUV5e3u3zjrdCP//88/r222+1fPlyrV+/Xjk5OdqzZ4/Kysq0evXqYGICAIB+Kqji0mH//v1au3atfve73ykhIUELFizokzCDBg3SunXrlJ+fr5UrV2rw4MHKy8vThAkT+uT5AQCAvfW6uPj9fpWXl+s3v/mN6urqdPr0ab366qu6+eabLyjA888/3+1zj8ejDRs2XNBzAgCA/qlX13F5/fXXlZ6eruXLlys9PV0fffSRYmJierx1GQAA4GLq1RGXgoICTZ8+XY899piioqIudiYAAICz6tURlyeffFI7d+7UxIkTtWLFCjU2Nna+qwgAACBUelVccnJy9M477+hXv/qV6uvrlZ6ermPHjmn79u1cKRAAAIRMUO8qSklJUUpKir755hu9+eabev7557Vs2TLdeeed3e7w3C/42k0ngBUwDgDAUs7r7dDDhw/XggUL9C//8i/asmWL3nzzzb7OZUQgEOj8OPrT/vFvQt/pOj4AAGYEdXfo74qKitLUqVO1cePGvsoDAABwTud1xKW/6jrh+GTydMk5wGAaWIKvvfPoGxPSAcA8isu5OAdQXAAAsBiKC2BxgUCg82ak4azrPmB/nOFyuTgSiLBDcQEszuv1KiMjw3QMS5k0aZLpCJawdetWud1u0zGAkLqgybkAAAChxBEXwEZe/n9HNdAZvm/L7nhHejifHWnzOZT7cazpGIAxFBfARgY6A3I5TaeAWeFbXAGJU0UAAMBGKC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2KC4AAMA2uMkiACAogUBAXq/XdAzjuu4D9scZLpdLjot8+3aKCwAgKF6vVxkZGaZjWMqkSZNMR7CErVu3yu12X9RtcKoIAADYBkdcAADnzZfpC+9XksCf/n9xz45Y22nJWeYM2ebCebgBAC5UpHglQUhxqggAANiGkeKyfft2ZWdna/To0UpNTdXixYs7Z2RXV1crOztbycnJSktLU0lJiYmIAADAgkJeXI4cOaL7779fd999t3bv3q1NmzZp165deu2119Tc3KzZs2dr8uTJqqqqUn5+vgoKClRTUxPqmAAAwIJCfmZy8ODB2rZtm2JiYhQIBHT06FG1tbVp8ODBqqioUGxsrHJyciRJKSkpyszMVHFxsZKSkkIdFQAAWIyRKVUxMTGSpIkTJ6qxsVFjxozRlClT9MILLygxMbHbY+Pj41VaWhr0Nnw+X0i+BuHD5/MZGSOMS5wLYxJWcyFjsrdfZ3QueEVFhZqbm/XII49ozpw5iouL63HhGpfLpZaWlqCfu7a2NuivaWtrC/prED5qamo0cODAkG+XcYlzYUzCakIxJo0WF5fLJZfLpQULFig7O1szZ87U8ePHuz3G6/UqOjo66Of2eDxyOoN7X3lra2vQ20H4SEpKuuhXhDwbxiXOhTEJq7mQMenz+Xp10CHkxWXv3r164okntGXLFkVFRUmSTp06pQEDBig+Pl6VlZXdHl9fX6+EhISgt+N0OoMuLsE+HuHlfMZUX223QxtH6MNe1zFghTEJdBWKMRny4jJy5Eh5vV4VFhZq/vz5OnTokJYuXaqpU6cqIyNDhYWFWr9+vXJycrRnzx6VlZVp9erVoY4JWEYgEOj8OPfjQQaTwGq6jg0gXIS8uERHR2vNmjVasmSJUlNTdemllyozM1O5ubmKiorSunXrlJ+fr5UrV2rw4MHKy8vThAkTQh0TAABYkJE5LvHx8Vq3bt1Z13k8Hm3YsCHEiQDr6nqL+Jf/37cayFH6sNbm+/ORt65jAwgX3GECsJGBTslFcQEQxrhXEQAAsA2KCwAAsA2KCwAAsA2KCwAAsA0m556L/7TpBGZ1XB8i3N+1EO7jAAAshuJyDtF7i01HAAAA38GpIgAAYBsccenC5XJp69atpmMY5/V6NWnSJEnS5s2b5XK5DCeyBvYDAJhHcenC4XAYudOqlblcLvYJAMAyOFUEAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+ICAABsg+u4AADOH7fzQojHAMUFABCUQMdNWCU5y5wGk8Bquo6Ni4VTRQAAwDY44gIACIrD4ej82Jfp45Uk3J3+85G3rmPjYmG4AQDOX6R4JUFIMdwAG2nzOSRd/HPIVtVx+jwEf9RZ1pkxAIQvigtgI7kfx5qOAABGMTkXAADYBkdcAItzuVzaunWr6RjGeb1eTZo0SZK0efNmuVwuw4nMYx8gHFFcAItzOBxyu92mY1iKy+VinwBhilNFAADANiguAADANiguAADANiguAADANiguAADANiguAADANowUly+//FKzZs3SuHHjlJqaqoULF+rIkSOSpOrqamVnZys5OVlpaWkqKSkxEREAAFhQyIuL1+vVfffdp+TkZH3yySd6++23dfToUT3xxBNqbm7W7NmzNXnyZFVVVSk/P18FBQWqqakJdUwAAGBBIb8AXUNDg6677jrl5ubK6XQqKipK06ZN08KFC1VRUaHY2Fjl5ORIklJSUpSZmani4mIlJSUFtR2fz3cx4oeFrvvO5/OxL2EJjEvrYN/jXC7kZ7O3Xxfy4nLttddqzZo13ZZt3bpV119/verq6pSYmNhtXXx8vEpLS4PeTm1t7QXlDGdtbW2dH9fU1GjgwIEG0wBnMC6to+v3AugqFD+bRi/5HwgE9MILL+jDDz9UUVGR/u3f/q3HZbxdLpdaWlqCfm6PxyOn09lXUcNKa2tr58dJSUlcWh2WwLi0jq7fC6CrC/nZ9Pl8vTroYKy4nDhxQo8//ri++OILFRUVaeTIkXK73Tp+/Hi3x3m9XkVHRwf9/E6nk+JynrruN/YjrIJxaR3se5xLKH42jbyr6Ouvv1ZWVpZOnDih0tJSjRw5UpKUmJiourq6bo+tr69XQkKCiZgAAMBiQl5cmpubdc8992j06NFau3atBg8e3LkuPT1dhw8f1vr169Xe3q4dO3aorKxMWVlZoY4JAAAsKOSnijZu3KiGhga99957Ki8v77bu008/1bp165Sfn6+VK1dq8ODBysvL04QJE0IdEwAAWFDIi8usWbM0a9asc673eDzasGFDCBMBAAC7MPquIgCAzZ02HcCwwJ/+7zCawqwQjwGKCwDgvDnLeIcRQoubLAIAANvgiAsAICgul0tbt241HcM4r9erSZMmSZI2b94sl8tlOJF5odgHFBcAQFAcDgdXLv4Ol8vFPgkRThUBAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADboLgAAADbMFpcjhw5ovT0dO3cubNzWXV1tbKzs5WcnKy0tDSVlJQYTAgAAKzEWHHZs2ePpk2bpq+//rpzWXNzs2bPnq3JkyerqqpK+fn5KigoUE1NjamYAADAQiJNbHTTpk1auXKlFixYoLlz53Yur6ioUGxsrHJyciRJKSkpyszMVHFxsZKSkoLahs/n69PM4aTrvvP5fOxLWALjElbDmOxbvd1/RorLTTfdpMzMTEVGRnYrLnV1dUpMTOz22Pj4eJWWlga9jdra2gvOGa7a2to6P66pqdHAgQMNpgHOYFzCahiTZhgpLldcccVZl588eVJut7vbMpfLpZaWlqC34fF45HQ6zytfuGttbe38OCkpqcf3BDCBcQmrYUz2LZ/P16uDDkaKy7m43W4dP3682zKv16vo6Oign8vpdFJczlPX/cZ+hFUwLmE1jEkzLPV26MTERNXV1XVbVl9fr4SEBEOJAACAlViquKSnp+vw4cNav3692tvbtWPHDpWVlSkrK8t0NAAAYAGWKi6DBg3SunXrVF5ervHjxysvL095eXmaMGGC6WgAAMACjM9x+eqrr7p97vF4tGHDBkNpAACAlVnqiAsAAMBfQnEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2QXEBAAC2EWk6AAB7CAQC8nq9xrbfddsmc0iSy+WSw+EwmgEIVxQXAN8rEAgoNzdXn3/+uekokqRJkyYZ3b7H49GqVasoL4ABnCoC0Cu8SAOwAo64APheDodDq1atMnqKJicnR4cPH+78/IorrlBRUZGRLJwqAsyhuADoFYfDIbfbbWTb7733XrfSIkmHDh3SRx99pJ/+9KdGMgEwg1NFACzN5/Np2bJlZ123bNky+Xy+ECcCYBLFBYClbdmy5ZzlxOfzacuWLSFOBMAkigsAS7vzzjvldDrPui4yMlJ33nlniBMBMIniAsDSnE6nsrOzz7pu2rRp5yw1APonigsAS/P7/Xr33XfPuq6srEx+vz/EiQCYRHEBYGnbt2/XsWPHzrru2LFj2r59e4gTATCJ4gLA0lJSUnTZZZeddd3ll1+ulJSUECcCYBLFBYClRURE6O677z7ruunTpysigl9jQDjhAnQWxM3suuMqpeHN7/frjTfeOOu6119/XdOmTaO8AGGE4mIx3MyuJ25oF962bdumlpaWs65raWnRtm3bdNNNN4U4FQBTLFlcmpqa9OSTT2rXrl1yOp2688479eijjyoy0pJx+xwv0MCfBQKBC1qP/ouj038WTkemLdkEHn74YcXFxen3v/+9Dh8+rAceeEDr16/XfffdZzraRWeFm9lJf34xsMIPQjj9QKKn4cOHX9B69E8cne4unI5MW6647Nu3T7t27dLHH38st9utK6+8Ug8++KCWL18eFsVFMnszO8BqrrnmGo0cOVJfffVVj3XXXXedrrnmGgOpYAXh8CKNnixXXOrq6hQbG6u4uLjOZX/913+thoYGHTt27Jxvi/wubrwG9B9PPvmkZsyY0WN5Xl4eF6ALYy+++CJHp//E5XLZ/meht6/blisuJ0+e7HG0oePzlpaWXheX2traPs8GwJyJEyfqd7/7Xefnt9xyiw4dOqRDhw4ZTAUg1CxXXC655BK1trZ2W9bxeXR0dK+fx+PxcA8ToB+57rrrtGvXLrW2tsrtduvRRx+Vy+UyHQtAH/H5fL066GC54pKQkKCjR4/q8OHDGjp0qCTpf//3fzVs2DBdeumlvX4ep9NJcQH6kejoaD311FN64YUX9PDDDwf1hwyA/sNyV226+uqrdcMNN2jJkiU6ceKE9u/fr9WrV2vq1KmmowEwLDU1VSUlJUpNTTUdBYAhlisukrRy5UqdPn1at956q+666y7dfPPNevDBB03HAgAAhlnuVJEkDR06VCtXrjQdAwAAWIwlj7gAAACcDcUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYhiUvQHchOm4x3tvbYwMAAPM6Xrc7XsfPpd8VF7/fL0m9usMkAACwlo7X8XNxBL6v2tiM3+/X6dOnFRERIYfDYToOAADohUAgIL/fr8jISEVEnHsmS78rLgAAoP9ici4AALANigsAALANigsAALANigsAALANigsAALANigsAALANigsAALANigvO6ciRI0pPT9fOnTtNR0GY+/LLLzVr1iyNGzdOqampWrhwoY4cOWI6FsLc9u3blZ2drdGjRys1NVWLFy+W1+s1Havfo7jgrPbs2aNp06bp66+/Nh0FYc7r9eq+++5TcnKyPvnkE7399ts6evSonnjiCdPREMaOHDmi+++/X3fffbd2796tTZs2adeuXXrttddMR+v3KC7oYdOmTXrkkUc0d+5c01EANTQ06LrrrlNubq6ioqI0aNAgTZs2TVVVVaajIYwNHjxY27Zt05QpU+RwOHT06FG1tbVp8ODBpqP1exQX9HDTTTfp/fff1x133GE6CqBrr71Wa9askdPp7Fy2detWXX/99QZTAVJMTIwkaeLEicrMzNQVV1yhKVOmGE7V/1Fc0MMVV1yhyMh+d+Nw9AOBQEArVqzQhx9+qEWLFpmOA0iSKioq9PHHHysiIkJz5swxHaffo7gAsIUTJ05ozpw5KisrU1FRkUaOHGk6EiBJcrlciouL04IFC/T73/9ezc3NpiP1axQXAJb39ddfKysrSydOnFBpaSmlBcbt3btXf/d3f6dTp051Ljt16pQGDBggt9ttMFn/R3EBYGnNzc265557NHr0aK1du5bJj7CEkSNHyuv1qrCwUKdOndI333yjpUuXaurUqYqKijIdr19jIgMAS9u4caMaGhr03nvvqby8vNu6Tz/91FAqhLvo6GitWbNGS5YsUWpqqi699FJlZmYqNzfXdLR+zxEIBAKmQwAAAPQGp4oAAIBtUFwAAIBtUFwAAIBtUFwAAIBtUFwAAIBtUFwAAIBtUFwAAIBtUFwAAIBtcOVcABdVWlqaDh061HnH8UAgoJiYGGVmZmrBggWKiDj3309paWl66KGHNGXKlFDFBWBxFBcAF90zzzzTrXx89dVXuvfee+V2uzVnzhyDyQDYDaeKAITcyJEjNXbsWP33f/+3Wlpa9OyzzyolJUVjxozRP//zP+ubb77p8TWNjY16+OGHlZaWph/96Ee69dZbVVpa2rn+zTff1G233aYxY8YoMzNTJSUlneteeuklTZw4UePGjVNWVpY++OCDkPw7AfQ9iguAkGpvb9fOnTu1Y8cOpaam6tlnn1Vtba02btyobdu2aejQoZo3b16Pr8vLy9OAAQP0zjvvaO/evZoxY4YWL16skydPav/+/SooKNBrr72m3bt3a+HChVq8eLEOHjyoHTt26N///d9VUlKinTt3Kjs7W4sWLVJ7e7uBfz2AC8WpIgAX3TPPPKMlS5Z0fj5s2DDNmjVL06ZN0w033KBXXnlFP/jBDyRJjz/+uPbt29fjOZ577jlFR0drwIABamhoUHR0tLxer5qbm+V0OhUIBLRhwwZlZGQoJSVFn332mSIiIvTNN9+oublZv/3tb/WTn/xE2dnZmjZtmhwOR8j+/QD6DsUFwEX3y1/+8qwTbA8dOqRTp07phz/8Yeeyyy67TB6Pp8dj9+/fr2XLlukPf/iDrr76al111VWSJL/frxEjRuiNN97QmjVr9POf/1w+n09TpkzRggULlJycrJdeeqlzvcvl0syZM/XAAw/8xYnBAKyJ4gLAmCFDhigqKkp//OMfde2110qSmpqa9Otf/1oPP/xw5+Pa29t1//33a968eZo+fbocDoc+//xzbdmypfNrfD6fXn75Zfn9fu3du1dz5szRNddco5/85CcaMmSI1q5dq1OnTmn79u166KGHdP311+uWW24x8K8GcCH4cwOAMREREZo8ebJeeuklNTY2qq2tTS+88II+++wzuVyuzse1t7fL6/XK5XLJ4XCooaFBy5cv71zX0NCgf/zHf9T27dsVERGhuLg4SdKgQYNUW1ur++67T19++aWioqI0ZMiQznUA7IcjLgCMeuyxx7RixQplZ2fL6/Vq3LhxevHFF7s95pJLLtGSJUv04osv6rnnntOQIUN01113qb6+Xv/zP/+jjIwMPfXUU3r66ad18OBBXXrppZo+fbp++tOfyuFw6A9/+IMeeOABffvttxoyZIieeOIJ/ehHPzL0LwZwIRyBQCBgOgQAAEBvcKoIAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYBsUFAADYxv8HBDVp4APOXvIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = df, y = 'Age' , x = 'Pclass' , )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8022e1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "115c504b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d96277a8",
   "metadata": {},
   "source": [
    "As, there are some outliers in 2nd and 3rd class passengers, it is better to replace null values with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ba2c59da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_null(cols):\n",
    "    age = cols[0]\n",
    "    pClass = cols[1]\n",
    "    if pd.isnull(age):\n",
    "        if pClass == 1:\n",
    "            return 37\n",
    "        elif pClass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 25\n",
    "    else:\n",
    "        return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "63439a71",
   "metadata": {},
   "outputs": [],
   "source": [
    " df['Age'] =df[['Age' , 'Pclass']].apply(fill_null , axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b79f81d",
   "metadata": {},
   "source": [
    "Cross Checking the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5b4fa633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Checking the null values')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna() , yticklabels = False)\n",
    "plt.title('Checking the null values')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78979284",
   "metadata": {},
   "source": [
    "No null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a02f80a",
   "metadata": {},
   "source": [
    "Now, lets see what other relationship can me drawn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "62a40c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare  Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500      0        S  \n",
       "1      0          PC 17599  71.2833      1        C  \n",
       "2      0  STON/O2. 3101282   7.9250      0        S  \n",
       "3      0            113803  53.1000      1        S  \n",
       "4      0            373450   8.0500      0        S  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d040b7c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Siblings and Spouses')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x = 'SibSp')\n",
    "plt.title('Number of Siblings and Spouses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b45b846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Parents and Children')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ur18/FRYW6pVXXpGTk5O6du3aJDUCEsEGsGI2m7Vo0SI9+eSTVu1TpkxRSUmJtm7dqurqag0ZMkSJiYmKjY1tksdt166d4uLi9PLLL+vbb79Vz549tX37dssp/9atWyslJUXLly/XSy+9pPPnz+u2227TM888o9/85jc2P95TTz2l9u3ba/v27UpNTZXZbNZDDz2kmTNn1tvj0FQefvhhnTlzRm+//bbefPNNdezYUffff78effRRLVy4UPn5+ZZPYv1YU84/MjJSH3zwgaZPn64ZM2Zo8uTJevnll7V161bLhuHg4GC98cYblo3ePzZlyhR99913euONN7Ru3Tp17txZERERcnJy0saNG1VaWnrNsHbJyJEj5ezsrPXr1+vdd9+Vn5+fnn/+eT399NPXPPbhhx/WnXfeqddff12rVq1ScXGxOnTooKCgIEtQbUpTpkxR69at9frrr+v1119XUFCQ5s6dq0WLFl3z2JkzZ6pDhw568803tXnzZrVr104hISF6+umnLWe9gKbgVMfV4AAAgEGwxwYAABgGwQYAABgGwQYAABgGwQYAABgGwQYAABgGwQYAABhGi/sem0tXIXZ2dr7iBe4AAIBjqaurU21trVxdXet9g/jlWlywuXjx4jWvSwMAABxTYGCg3N3dr9jf4oLNpZQXGBho0/VlAACA/dTU1CgnJ+eqZ2skOwSbP/3pT0pISLBqq66uliR98sknOnHihF544QXl5+fLy8tLsbGxio6OtoxNT0/X+vXr9e233+quu+7SwoULFRQU1OjHv7T85OLiQrABAOAmc61tJM2+eTg8PFzHjh2z/OzZs0dms1mJiYkqLS3V5MmTNXr0aGVnZysxMVFLly7VyZMnJUlZWVlavHixli1bpuzsbIWHhys2Ntami84BAADjsuunourq6hQXF6chQ4YoIiJCe/fuldlsVkxMjFxdXRUSEqJRo0YpJSVFkpSamqoRI0YoODhYbm5umjhxory8vLR79257TgMAADgIu+6xeffdd5Wfn6/169dLkvLy8uTn52c1xsfHR2lpaZKk/Px8jRkzpl5/bm6uzY9dU1NznVUDAIDm1tjXbbsFm9raWm3YsEFTp05VmzZtJEllZWUymUxW4zw8PFReXt6oflvwySgAAIzHbsEmKytLZ8+eVVRUlKXNZDLp/PnzVuMqKyvl6elp6a+srKzX7+XlZfPj86koAABuHpc+FXUtdgs27733nkJDQ9W6dWtLm5+fnz766COrcfn5+fL19ZUk+fr6Ki8vr17/4MGDbX58PhUFAIDx2G3z8JEjR3TvvfdatYWGhqqoqEjJycmqrq5WZmamMjIyLPtqoqKilJGRoczMTFVXVys5OVnFxcUKDQ21xxQAAICDsdsZmzNnzujWW2+1avPy8tLWrVuVmJio1atXy9vbW/Hx8erfv78kKSQkRAkJCVq0aJEKCwvl4+OjpKQkmc1mO8wAAAA4Gqe6uro6exfRnGpqanT8+HH16tWLpSgAAG4SjX395ureAADAMAg2AADAMAg2AADAMAg2AADAMAg2AADAMAg2AADAMAg2P1JTW2vvEm4oo88PANCy2fXq3o7IxdlZ8W8e0BdnS+1dSpP7/25tpxceHWTvMgAAuGEINg344mypcr8usXcZAADARixFAQAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAwyDYAAAAw7BLsDl37pzmzJmjfv366d5779W0adN09uxZSdKJEycUHR2toKAgDRs2TKmpqVbHpqenKzQ0VL169VJkZKSOHTtmjykAAAAHZJdg89RTT6m8vFz79u3T/v375eLiooULF6q0tFSTJ0/W6NGjlZ2drcTERC1dulQnT56UJGVlZWnx4sVatmyZsrOzFR4ertjYWFVUVNhjGgAAwME0e7D55JNPdOLECS1btky33HKL2rRpo8WLF2v27Nnau3evzGazYmJi5OrqqpCQEI0aNUopKSmSpNTUVI0YMULBwcFyc3PTxIkT5eXlpd27dzf3NAAAgANybe4HPHnypHx8fPTWW2/pD3/4gyoqKjRo0CDNnTtXeXl58vPzsxrv4+OjtLQ0SVJ+fr7GjBlTrz83N9fmOmpqahpsd3Fxsfm+bjZXmjsAAI6qsa9dzR5sSktLderUKd1zzz1KT09XZWWl5syZo7lz56p9+/YymUxW4z08PFReXi5JKisru2q/LXJycuq1mUwmBQQE2HxfN5tTp06xfAcAMKRmDzbu7u6SpAULFqhVq1Zq06aNZs6cqbFjxyoyMlKVlZVW4ysrK+Xp6Snph+DRUL+Xl5fNdQQGBraIszMN8ff3t3cJAADYpKampsGTEj/W7MHGx8dHtbW1qq6uVqtWrSRJtbW1kqRu3brpzTfftBqfn58vX19fSZKvr6/y8vLq9Q8ePNjmOlxcXFpssGmp8wYAGF+zbx6+7777dMcdd2j+/PkqKytTSUmJVq5cqeHDh2vkyJEqKipScnKyqqurlZmZqYyMDMu+mqioKGVkZCgzM1PV1dVKTk5WcXGxQkNDm3saAADAATV7sHFzc9O2bdvk4uKisLAwhYWFqVOnTlqyZIm8vLy0detW7dmzR/369VN8fLzi4+PVv39/SVJISIgSEhK0aNEi9e3bV3/+85+VlJQks9nc3NMAAAAOqNmXoiSpY8eOWrlyZYN9gYGB2rFjxxWPjYiIUERExI0qDQAA3MS4pAIAADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMuwSb3bt3KyAgQEFBQZafuLg4SdKJEycUHR2toKAgDRs2TKmpqVbHpqenKzQ0VL169VJkZKSOHTtmjykAAAAH5GqPB83JyVFERISWLl1q1V5aWqrJkydrxowZeuSRR5Sdna3p06fL399fPXr0UFZWlhYvXqykpCT16NFDKSkpio2N1f79+2UymewxFQAA4EDscsYmJydH99xzT732vXv3ymw2KyYmRq6urgoJCdGoUaOUkpIiSUpNTdWIESMUHBwsNzc3TZw4UV5eXtq9e3dzTwEAADigZj9jU1tbq08//VQmk0mbN29WTU2N7r//fs2ePVt5eXny8/OzGu/j46O0tDRJUn5+vsaMGVOvPzc31+Y6ampqGmx3cXGx+b5uNleaOwAAjqqxr13NHmxKSkoUEBCgsLAwrV69Wt99953mzp2ruLg4dejQod6SkoeHh8rLyyVJZWVlV+23RU5OTr02k8mkgIAAm+/rZnPq1ClVVFTYuwwAAJpcsweb9u3bW5aWpB/CRFxcnMaOHavIyEhVVlZaja+srJSnp6dlbEP9Xl5eNtcRGBjYIs7ONMTf39/eJQAAYJOampoGT0r8WLMHm9zcXO3atUvPPPOMnJycJElVVVVydnZWjx499Prrr1uNz8/Pl6+vryTJ19dXeXl59foHDx5scx0uLi4tNti01HkDAIyv2TcPm81mpaSkaPPmzbp48aIKCgr00ksv6eGHH1ZYWJiKioqUnJys6upqZWZmKiMjw7KvJioqShkZGcrMzFR1dbWSk5NVXFys0NDQ5p4GAABwQM1+xqZTp07auHGjVqxYoQ0bNqhVq1YaMWKE4uLi1KpVK23dulWJiYlavXq1vL29FR8fr/79+0uSQkJClJCQoEWLFqmwsFA+Pj5KSkqS2Wxu7mkAAAAHZJfvsenbt6927NjRYF9gYOAV+yQpIiJCERERN6o0AABwE+OSCgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDDsGmxqamo0fvx4zZs3z9J24sQJRUdHKygoSMOGDVNqaqrVMenp6QoNDVWvXr0UGRmpY8eONXfZAADAQdk12Kxdu1aHDx+23C4tLdXkyZM1evRoZWdnKzExUUuXLtXJkyclSVlZWVq8eLGWLVum7OxshYeHKzY2VhUVFfaaAgAAcCB2CzYHDx7U3r179eCDD1ra9u7dK7PZrJiYGLm6uiokJESjRo1SSkqKJCk1NVUjRoxQcHCw3NzcNHHiRHl5eWn37t32mgYAAHAgrvZ40OLiYi1YsEDr169XcnKypT0vL09+fn5WY318fJSWliZJys/P15gxY+r15+bm2lxDTU1Ng+0uLi4239fN5kpzBwDAUTX2tavZg01tba3i4uI0adIkde3a1aqvrKxMJpPJqs3Dw0Pl5eWN6rdFTk5OvTaTyaSAgACb7+tmc+rUKZbvAACG1OzBZuPGjXJ3d9f48ePr9ZlMJp0/f96qrbKyUp6enpb+ysrKev1eXl421xEYGNgizs40xN/f394lAABgk5qamgZPSvxYswebd999V2fPnlWfPn0kyRJU/vrXv2rOnDn66KOPrMbn5+fL19dXkuTr66u8vLx6/YMHD7a5DhcXlxYbbFrqvAEAxtfsm4f37Nmjo0eP6vDhwzp8+LBGjhypkSNH6vDhwwoNDVVRUZGSk5NVXV2tzMxMZWRkWPbVREVFKSMjQ5mZmaqurlZycrKKi4sVGhra3NMAAAAOyC6bh6/Ey8tLW7duVWJiolavXi1vb2/Fx8erf//+kqSQkBAlJCRo0aJFKiwslI+Pj5KSkmQ2m+1bOAAAcAh2DzbLli2zuh0YGKgdO3ZccXxERIQiIiJudFkAAOAmxCUVAACAYRBsAACAYdgcbGJjYxtsHzdu3E8uBgAA4Kdo1B6bM2fO6J133pEkffjhh1q7dq1V/4ULF3Tq1KkmLw4AAMAWjQo2P//5z5WXl6eSkhLV1NQoKyvLqr9Vq1ZKSEi4IQUCAAA0VqOCjbOzs1555RVJUnx8vF544YUbWhQAAMD1sPnj3i+88IKqqqpUUlKi2tpaq76f//znTVYYAACArWwONnv27NHChQt14cIFS1tdXZ2cnJz0v//7v01aHAAAgC1sDjarV69WTEyMHn74Ybm62v37/QAAACxsTib/+c9/9OSTTxJqAACAw7H5e2y6d++u/Pz8G1ELAADAT2LzaZfevXtr4sSJeuihh9S+fXurvieffLLJCgMAALCVzcHm2LFj8vX11eeff67PP//c0u7k5NSkhQEAANjK5mCzbdu2G1EHAADAT2ZzsLl0aYWGjB49+ieUAgAA8NNc18e9L1daWqqKigoFBwcTbAAAgF3ZHGz+/ve/W92uq6tTUlKSzp0711Q1AQAAXBebP+79Y05OTvo//+f/6N13322KegAAAK7bTw42kvTFF1/wqSgAAGB3Ni9FjR8/3irEVFdX69SpUwoPD2/SwgAAAGxlc7Dp16+f1W1nZ2dNnDhRw4cPb7KiAAAArofNwebybxcuLi5Wu3btuG4UAABwCDbvsamurtaSJUsUFBSkgQMHKjg4WAsXLlRVVdWNqA8AAKDRbA4269evV1ZWllatWqVdu3Zp1apVOnHihFatWnUDygMAAGg8m9eQMjIy9Nprr+mOO+6QJN199926++67FRMTozlz5jR5gQAAAI1l8xmb0tJSde7c2aqtc+fOqqysbLKiAAAArofNwcbf3187duywatuxY4f8/PyarCgAAIDrYfNS1MyZM/Wb3/xGf/rTn3THHXfo9OnTys/P15YtW25EfQAAAI1mc7Dp06ePFixYoBMnTsjV1VVDhw7V2LFj1bt37xtRHwAAQKNd19W909PT9dprr6lLly7629/+piVLlqi0tFSPP/74jagRAACgUWzeY5OWlqY33nhDXbp0kSQ98MADeu2115SSktLUtQEAANjE5mBz4cKFBj8VVV5e3mRFAQAAXA+bg0337t21adMmq7atW7eqa9euTVYUAADA9bB5j828efP0m9/8Rm+99ZY6deqkb775RhcvXtTmzZtvRH0AAACNZnOw6d69u/bu3av9+/fr7Nmz6ty5s4YMGaK2bdveiPoAAAAa7bouy92uXTuNHj26iUsBAAD4aWzeYwMAAOCoCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAw7BJsDh48qOjoaPXu3VsDBgzQ4sWLVVlZKUk6ceKEoqOjFRQUpGHDhik1NdXq2PT0dIWGhqpXr16KjIzUsWPH7DEFAADggJo92JSUlGjKlCn69a9/rcOHDys9PV2HDh3Spk2bVFpaqsmTJ2v06NHKzs5WYmKili5dqpMnT0qSsrKytHjxYi1btkzZ2dkKDw9XbGysKioqmnsaAADAATV7sPH29tbHH3+syMhIOTk56dy5c/rvf/8rb29v7d27V2azWTExMXJ1dVVISIhGjRpluXJ4amqqRowYoeDgYLm5uWnixIny8vLS7t27m3saAADAAV3XNw//VG3atJEk3X///SosLFSfPn0UGRmpVatWyc/Pz2qsj4+P0tLSJEn5+fkaM2ZMvf7c3Fyba6ipqWmw3cXFxeb7utlcae4AADiqxr522SXYXLJ3716VlpZq9uzZmjFjhjp27CiTyWQ1xsPDQ+Xl5ZKksrKyq/bbIicnp16byWRSQECAzfd1szl16hTLdwAAQ7JrsPHw8JCHh4fi4uIUHR2t8ePH6/z581ZjKisr5enpKemH4HFpk/Hl/V5eXjY/dmBgYIs4O9MQf39/e5cAAIBNampqGjwp8WPNHmyOHj2q+fPn609/+pPc3d0lSVVVVXJzc5OPj48++ugjq/H5+fny9fWVJPn6+iovL69e/+DBg22uw8XFpcUGm5Y6bwCA8TX75mF/f39VVlZq+fLlqqqq0tdff60XX3xRUVFRCgsLU1FRkZKTk1VdXa3MzExlZGRY9tVERUUpIyNDmZmZqq6uVnJysoqLixUaGtrc0wAAAA6o2c/YeHp6avPmzVqyZIkGDBigtm3batSoUZo+fbrc3d21detWJSYmavXq1fL29lZ8fLz69+8vSQoJCVFCQoIWLVqkwsJC+fj4KCkpSWazubmnAQAAHJBd9tj4+Pho69atDfYFBgZqx44dVzw2IiJCERERN6o0AABwE+OSCgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDAINgAAwDDsEmxyc3M1adIk9e3bVwMGDNCcOXNUUlIiSTpx4oSio6MVFBSkYcOGKTU11erY9PR0hYaGqlevXoqMjNSxY8fsMQUAAOCAmj3YVFZW6vHHH1dQUJA+/PBD7dq1S+fOndP8+fNVWlqqyZMna/To0crOzlZiYqKWLl2qkydPSpKysrK0ePFiLVu2TNnZ2QoPD1dsbKwqKiqaexoAAMABNXuwKSgoUNeuXTV9+nS5u7vLy8tLjzzyiLKzs7V3716ZzWbFxMTI1dVVISEhGjVqlFJSUiRJqampGjFihIKDg+Xm5qaJEyfKy8tLu3fvbu5pAAAAB+Ta3A941113afPmzVZt7733nrp37668vDz5+flZ9fn4+CgtLU2SlJ+frzFjxtTrz83NtbmOmpqaBttdXFxsvq+bzZXmDgCAo2rsa1ezB5vL1dXVadWqVdq/f7+2b9+uN954QyaTyWqMh4eHysvLJUllZWVX7bdFTk5OvTaTyaSAgACb7+tmc+rUKZbvAACGZLdgc+HCBT377LP69NNPtX37dvn7+8tkMun8+fNW4yorK+Xp6Snph+BRWVlZr9/Ly8vmxw8MDGwRZ2ca4u/vb+8SAACwSU1NTYMnJX7MLsHm9OnTeuKJJ/Tzn/9caWlp8vb2liT5+fnpo48+shqbn58vX19fSZKvr6/y8vLq9Q8ePNjmGlxcXFpssGmp8wYAGF+zbx4uLS3VhAkT1Lt3b23ZssUSaiQpNDRURUVFSk5OVnV1tTIzM5WRkWHZVxMVFaWMjAxlZmaqurpaycnJKi4uVmhoaHNPAwAAOKBmP2Ozc+dOFRQU6C9/+Yv27Nlj1Xfs2DFt3bpViYmJWr16tby9vRUfH6/+/ftLkkJCQpSQkKBFixapsLBQPj4+SkpKktlsbu5pAAAAB+RUV1dXZ+8imlNNTY2OHz+uXr16XXFJJmbVLuV+XdLMld14XW/zVsrMkfYuAwAAmzXm9VvikgoAAMBACDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDYAAMAwCDZolLraGnuXcMO1hDkCgNG52rsA3BycnF1UtHOeqov+Ze9Sbgi39nepfeQye5cBAPiJCDZotOqif6n6m/+1dxkAAFwRS1EAAMAwCDYAAMAwCDYAAMAw7BpsSkpKFBoaqqysLEvbiRMnFB0draCgIA0bNkypqalWx6Snpys0NFS9evVSZGSkjh071txlAwAAB2W3YHPkyBE98sgjOn36tKWttLRUkydP1ujRo5Wdna3ExEQtXbpUJ0+elCRlZWVp8eLFWrZsmbKzsxUeHq7Y2FhVVFTYaxoAAMCB2CXYpKena/bs2Zo1a5ZV+969e2U2mxUTEyNXV1eFhIRo1KhRSklJkSSlpqZqxIgRCg4OlpubmyZOnCgvLy/t3r3bHtMAAAAOxi4f9x44cKBGjRolV1dXq3CTl5cnPz8/q7E+Pj5KS0uTJOXn52vMmDH1+nNzc22uoaam4S9jc3Fxsfm+bjZXmvvVtITnRbq+5wYAcOM19v9nuwSbDh06NNheVlYmk8lk1ebh4aHy8vJG9dsiJyenXpvJZFJAQIDN93WzOXXqlE3Ldy3leZFsf24AAI7Fob6gz2Qy6fz581ZtlZWV8vT0tPRXVlbW6/fy8rL5sQIDA1vMWYgf8/f3t3cJDovnBgAcU01NTYMnJX7MoYKNn5+fPvroI6u2/Px8+fr6SpJ8fX2Vl5dXr3/w4ME2P5aLi0uLDTYtdd6NwXMDADc3h/oem9DQUBUVFSk5OVnV1dXKzMxURkaGZV9NVFSUMjIylJmZqerqaiUnJ6u4uFihoaF2rhwAADgChzpj4+Xlpa1btyoxMVGrV6+Wt7e34uPj1b9/f0lSSEiIEhIStGjRIhUWFsrHx0dJSUkym832LRwAADgEuwebU6dOWd0ODAzUjh07rjg+IiJCERERN7osAABwE3KopSgAAICfgmADAAAMg2ADAAAMg2ADAAAMg2AD/EQ1tca/DENLmCMAY7D7p6KAm52Ls4t+997v9OV3X9q7lBuii1cXJYQl2LsMAGgUgg3QBL787kt99u1n9i4DAFo8lqIAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBhEGwAAIBh3JTBpri4WNOmTVOfPn3Ur18/JSYm6uLFi/YuCwAA2NlNGWxmzpyp1q1b68CBA0pLS9PBgweVnJxs77IAAICd3XTB5quvvtKhQ4cUFxcnk8mkO+64Q9OmTVNKSoq9SwPwI3U1NfYu4YZrCXNsbrW1tfYu4YZrCXO0F1d7F2CrvLw8mc1mdezY0dJ29913q6CgQN9//71uueUWO1YH4HJOLi7638WLVf7VV/Yu5YZofeed6rZw4XUdW1tbJ2dnpyauyLFc7xydnZ31zvpXVFxw5gZUZX8/+/ntGj3tt/Yuw7BuumBTVlYmk8lk1Xbpdnl5+TWDTV1dnSSpqqpKLi4u9fpdXFzk26md3F2M9x/OnR1uUU1NjWqu4x2mi4uLXDr4qdbZ/QZUZn8uP+vyk54bH28fuTm73YDK7O9O850/6bmpc3WV3Iz53NS5uv6k5yZ7X77On6u4AZXZX1uzSb2H+aiq6vqeGycXFzm73nQvUY3i5OJy3X83zk7OcjJwIK6rrVNtXcNnsy49X5dex6/Eqe5aIxzMvn37FB8fr6ysLEvbqVOnFB4ersOHD6tt27ZXPb6qqko5OTk3ukwAAHADBAYGyt39ym+yb7o47Ovrq3PnzqmoqEjt27eXJH3++efq1KnTNUONJLm6uiowMFDOzs5ycjJu6gUAwEjq6upUW1sr12ucybvpzthI0qOPPqpOnTrp+eef13fffafY2FiFhYXpqaeesndpAADAjm7KYFNUVKTnn39eWVlZcnZ21ujRozV79uwG98wAAICW46YMNgAAAA256b7HBgAA4EoINgAAwDAINgAAwDAINgAAwDAINgAAwDAINnZSXFysadOmqU+fPurXr58SExN18eJFe5flUEpKShQaGmr1LdMtWW5uriZNmqS+fftqwIABmjNnjkpKSuxdlkM4ePCgoqOj1bt3bw0YMECLFy9WZWWlvctyKDU1NRo/frzmzZtn71Icxu7duxUQEKCgoCDLT1xcnL3Lcgjnzp3TnDlz1K9fP917772aNm2azp49a++yGoVgYyczZ85U69atdeDAAaWlpengwYNKTk62d1kO48iRI3rkkUd0+vRpe5fiECorK/X4448rKChIH374oXbt2qVz585p/vz59i7N7kpKSjRlyhT9+te/1uHDh5Wenq5Dhw5p06ZN9i7Noaxdu1aHDx+2dxkOJScnRxERETp27Jjl56WXXrJ3WQ7hqaeeUnl5ufbt26f9+/fLxcVFC6/zgq/NjWBjB1999ZUOHTqkuLg4mUwm3XHHHZo2bZpSUlLsXZpDSE9P1+zZszVr1ix7l+IwCgoK1LVrV02fPl3u7u7y8vLSI488ouzsbHuXZnfe3t76+OOPFRkZKScnJ507d07//e9/5e3tbe/SHMbBgwe1d+9ePfjgg/YuxaHk5OTonnvusXcZDueTTz7RiRMntGzZMt1yyy1q06aNFi9erNmzZ9u7tEYh2NhBXl6ezGazOnbsaGm7++67VVBQoO+//96OlTmGgQMHat++ffqf//kfe5fiMO666y5t3rzZ6tu133vvPXXv3t2OVTmONm3aSJLuv/9+jRo1Sh06dFBkZKSdq3IMxcXFWrBggZYvXy6TyWTvchxGbW2tPv30U73//vsaOnSoBg8erIULF6q0tNTepdndyZMn5ePjo7feekuhoaEaOHCgXnzxRXXo0MHepTUKwcYOysrK6v0Hc+l2eXm5PUpyKB06dLjmRc5asrq6Oq1cuVL79+/XggUL7F2OQ9m7d6/+8Y9/yNnZWTNmzLB3OXZXW1uruLg4TZo0SV27drV3OQ6lpKREAQEBCgsL0+7du7Vjxw59+eWX7LGRVFpaqlOnTunLL79Uenq63nnnHRUWFmru3Ln2Lq1RePWwg9atW6uiosKq7dJtT09Pe5SEm8SFCxf07LPP6tNPP9X27dvl7+9v75IcioeHhzw8PBQXF6fo6GiVlpaqXbt29i7LbjZu3Ch3d3eNHz/e3qU4nPbt21st/5tMJsXFxWns2LG6cOGC5SxgS+Tu7i5JWrBggVq1aqU2bdpo5syZGjt2rMrKyhz+dYozNnbg6+urc+fOqaioyNL2+eefq1OnTmrbtq0dK4MjO336tMaMGaMLFy4oLS2NUPP/HD16VA899JCqqqosbVVVVXJzc2vxSy/vvvuuDh06pD59+qhPnz7atWuXdu3apT59+ti7NLvLzc3Vyy+/rMsvl1hVVSVnZ2fLC3tL5ePjo9raWlVXV1vaamtrJUk3w+UlCTZ20KVLFwUHB2vJkiW6cOGC/v3vf2v9+vWKioqyd2lwUKWlpZowYYJ69+6tLVu2sDH2Mv7+/qqsrNTy5ctVVVWlr7/+Wi+++KKioqJa/AvUnj17dPToUR0+fFiHDx/WyJEjNXLkSD4dJclsNislJUWbN2/WxYsXVVBQoJdeekkPP/xwi/+7ue+++3THHXdo/vz5KisrU0lJiVauXKnhw4ffFGeyCDZ2snr1al28eFEPPPCAxo4dq0GDBmnatGn2LgsOaufOnSooKNBf/vIXBQcHW33vRkvn6empzZs3Ky8vTwMGDND48eN133338VF4XFWnTp20ceNG/e1vf1Pfvn01ZswYBQYG6rnnnrN3aXbn5uambdu2ycXFRWFhYQoLC1OnTp20ZMkSe5fWKE51N8N5JQAAgEbgjA0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMgg0AADAMrhUFwCEMGzZM3377reUCqHV1dWrTpo1GjRqluLg4OTs33fuwNWvW6NChQ9q2bVuT3ScAx0CwAeAwfve73ykyMtJy+9SpU5o4caJMJhNX6wbQKCxFAXBY/v7+uvfee/XPf/5ThYWFmjlzpoYNG6aePXvqgQceUFpamtXYF154Qf369dPUqVMlSRkZGRo5cqSCgoL0y1/+Urt377aMLysrU3x8vAYOHKh+/fpp5cqVzT4/AE2PYAPAIVVXVysrK0uZmZkaMGCA4uPj5ebmpj//+c86evSoxo0bp8WLF6usrMxyzOnTp/X+++/r97//vbKysjR//nzFxcXpyJEjevbZZzVnzhzl5+dLkv75z3/q3nvv1YEDB/TKK69o48aNOnbsmL2mC6CJsBQFwGH87ne/s7rQXqdOnTRp0iSNGzdODz74oDw9PeXm5qaCggJ5enqqsrJSpaWl8vT0lCSNHDlSJpNJJpNJ77zzjh588EHdf//9kqTBgwfrzTffVMeOHSVJvr6+ioiIkCT1799f7du31+nTp7mwKHCTI9gAcBgJCQlWe2wu9+9//1u///3v9eWXX6pLly668847JUm1tbWWMbfeeqvl32fPnlVAQIDVffTo0cPyb7PZbNXn7u6umpqanzoFAHbGUhQAh1ddXa0pU6YoIiJCWVlZeuuttzRhwoR645ycnCz/7ty5swoKCqz6t27dqqNHj97wegHYD8EGgMOrrq5WZWWlPDw85OTkpIKCAr300kuWvoY8/PDD2rdvnz788EPV1tbqwIEDWrNmjdq2bducpQNoZgQbAA6vdevWWrJkidatW6egoCA99thjGjBggNq3b6/PPvuswWOCg4P14osv6sUXX1SfPn30+9//XitWrJCvr28zVw+gOTnV1dXV2bsIAACApsAZGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBgEGwAAYBj/F+D9WVmzDvT4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x = 'Parch')\n",
    "plt.title('Number of Parents and Children')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10faab1f",
   "metadata": {},
   "source": [
    "Majority of the passesngers are travelling alone"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1b19ec",
   "metadata": {},
   "source": [
    "### Now, lets see the distribution of Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bdfa02cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fare Distribution')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = df, x = 'Fare' , kde = True , hue = 'Pclass' , palette = 'Set1')\n",
    "plt.ylim(0,200)\n",
    "plt.xlim(0,400)\n",
    "plt.title(\"Fare Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3ba06a",
   "metadata": {},
   "source": [
    "Price of 1st passenger class is high in comparison to 2nd and 3rd class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5720ad",
   "metadata": {},
   "source": [
    "Lastly, let's check the correlation between the numerical datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d5ad4234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation using Heatmap')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0idWrV1OoUCECAgIYOnSoruGzbNkypk2bxqhRozAzM6NChQosX76c6dOnc/LkSbp06ULFihX54IMP2LhxIwcPHmTPnj2vlY+trS3Lli1j9uzZDBo0CGtra9zd3dmwYQM9e/bk5MmTuonlAJMmTWL69OnExcVRvXp1Nm/eTOnSpY3y3Ajxb6PQyq8nCiH+4+Lj4zlw4ACBgYF686pmzZrFzp0781y19//R4sWLWbJkCVeuXCnoVIR4a0hPkhDiP8/S0pJp06bh7u7OJ598gpWVFadPn2b9+vX06dOnoNMTQrylpJEkhPjPMzc3Z926dXz55ZeMGjWK1NRUSpUqxahRo+jcuXNBpyeEeEvJcJsQQgghhAHyFQBCCCGEeKvExcXx7rvvvnC+4N9//02zZs3w9vamcePGuqtVjUkaSUIIIYR4a5w6dYoOHTpw9+7d58bcvn2bAQMGMGjQIE6ePMmAAQMYPHgwDx48MGou0kgSQgghxFvh+++/Z/jw4QwZMuQf4/z8/HjnnXcwMTGhSZMm1KhRg61btxo1H2kkCSGEECLfZGRkkJSUpHfLyMgwGFu3bl1+++03mjRp8sJ9Xr9+Pc+PhVeoUIGwsDCj5Q1yddtbIzPmZkGn8MZ2Vhtf0CkYRbpS8c9B/wIOanVBp/DGok2M89McBc1O/e+/Pkb1H7nG57/SM9Dy/qZ83b8x/yet3LyXJUuW6C3r378/AwYMyBP7sl9Ym5ycnOeLei0sLEhJSXn9RA2QRpIQQggh9GmM9yGrd+/eeX7SyMzM7I32aWlpSVpamt6ytLQ0vZ84MgZpJAkhhBAi35iZmb1xoyi3SpUqcfHiRb1l169fx8PDw6iP81/peRRCCCGEsWg1xrvlg+bNm3P8+HF++uknsrKy+Omnnzh+/DgtWrQw6uNII0kIIYQQ+jQa492MxMfHh927dwNQvnx5li5dysqVK6lRowbLli1j8eLFlC1b1miPBzLcJoQQQohctPnUA/Qqcv/Y8pkzZ/TuBwYGEhgYmK85SE+SEEIIIYQB0pMkhBBCCH1GHCb7N5NGkhBCCCH0vQXDbW8DGW4TQgghhDBAepKEEEIIoc+IXyb5byaNJCGEEELok+E2QIbbhBBCCCEMkp4kIYQQQuiTq9sAaSQJIYQQIpe34csk3wYy3CaEEEIIYYD0JAkhhBBCnwy3AdJIEkIIIURuMtwGvGIjKTg4mIcPH2Jikr2ZVqvFxsaGZs2aMWLECJTK//7oXUhICB9//HGeH9572fUFJe5RPJ17D2XyqMHUrO5Z0OlQLNgLz3EfYlPamZTIWEKnbObe72cMxiqUCjzHdqRMu0BUlmZEH7rEyZFrSYuOB8Dc0Q6/uZ9SJMAdrVrDnR2HCJ28Ca06+yT3ndmNsh2D0GQ9/d6P0EkbuLnhzzeqwTXYi+pjOmJT2pnkyFhOfbGZyN9Dn1tD9TEdKde2LiaWZtw/fIljo74mNacGlzpV8BnVnkIVXVGnpnNnz3FOTduMOi0TAP8ZXanQQb+Gk5M3cm3jm9Vg5mSH15weONaugjZLQ8SOQ1yavEH33D2rSENv3Md9iFXpIqRGxHJp6kaif8t+zUwLWeMxrStFGnihMFMRH3qTS5M28PjiHQDsfcpTd+8U1KkZuv0lnLvFkVZT3ij/Z5UI9qLGmI7Ylsp+PY5/sZnw/aEGYxVKBX5jOlKxTV1UlmbcO3yJw6Ofvh5lm/lTf3E/1OmZum3u/HKSvwetAKBc81r4DGmFlUthUh/Gc2HVz4Rt+OOVc3bJOQ+sc86Dcy84D8g5D0o/cx6cznUe+M79FOdnzoNzz5wHTxT2rUD97WPZWbabbtl7f83CuoSTXpyJtQXnp28lbPHul67H3MkOnzk9cKrtjjZLQ/iOQ5yfvNHg8VS0oTce4zpiXboIKRGxXJi6ifu/5a3dc+rHmNpZcmrQSt2yQlVLU23yRzh4lkWTqebBH6Gcm7CejEdJL53rqzB7pi5Nznly4QV1VcmpKzWnrgc5dSnNTak6tiPFP6iJiY0lidejuDRtCzGHL+VL3m9EvicJeI05SZMnT+bMmTOcOXOG0NBQ1qxZww8//MCSJUvyIz9hBKfPXaRz76GER94r6FQAsClblNqrB3Nh9jZ2VurJhTk7qL1qAJYuDgbjqwxuhUtQNX59fxy7fQagTsugxrweuvUBKweQlZzGbp/+/NZ4PEUDPajUq7FufWHvcpwcsYadFT7V3d60gWRbtihBqwYROmc7Wyr34uy8nQSteH4N1Qa1pFiQB3ubjGe770Cy0jIImJtdg3lhW4K/Gc7V9fvZ4t6LPY3GUTTAHY/Pmum2d/Qqx9GRa9lcqYfu9qYNJADflQPJSk7nN+9+HGw8Dqd6HpTr3SRPnHVZF/xWD+HKrG38UvFTrszdjt/KQVjk1Os1rycmtpbsDxjMPvdexJ+5QY1vhum2t/cpT+zRy/xcvpvuZswGkl3ZojRcNYjTc7az3r0Xp+ftJHjFAKye83p4D2qJaz0PdjUZzxa/gajTMgic8/SYcvYqx/Udh/nWrYfu9qSB5OBWgrpze3Bg2CrWu/fkwJBV1JrchaI13V4p52fPgx8q9eTinB3UWjVA95zmVmVwK4oGVeP398exJ+c88HvmPKiVcx7s8enP/pzzoOIz5wFAmY5B1NsyCpWFmd7yX+uP5PsKn+puV1f9zKPzt7m2Zt8r1VQzJ4efvT/jz8bjca7nQYXnHE+1Vg/m0qzt/FixB5fnbqfmyoF6tZs52OC3pB8Ver6vt63CVEXtjZ8Tc/gSe9x78WvAECyKOlBt8kevlOurqJFT1y/en/F3Tl3ln1NXzdWDCZu1nb05ddV4pq6qYztSuGYlDnwwkb2Ve3Jn45/UWj8cS1fHfMtdvJk37vpxc3OjRo0aXLp0iQcPHjB48GCCg4Px8vKiYcOGbN++XRe7adMm3nnnHfz8/GjWrBnbtm3TrVu8eDFBQUHUrFmTNm3asH//ft26ixcv0qVLF2rUqMF7773HunXr0Gq1uu0GDhzI8OHD8fPzo169esybN0+3bVpaGhMnTqRmzZoEBQXx5ZdfEhwcTEhICAAxMTEMHz6cOnXqULduXSZMmEBSUvankZCQEIKCghg2bBh+fn6sWrUqT/3R0dH06dOH6tWr07BhQw4fPvymT6lR7frpN0ZOms3AXp8UdCo6ZdrXI+b4FSJ/OYVWrSH8xxAeHg2j3EfBBuPLdarP5aV7SI2KIyspldPjv6VYsBfWpZyxKVOUonWqcHbqZtSpGSTffcjFBT9Qsft7ACjNTChUuSRxZ28atYby7QKJPn6F8H3ZNdz5MYQHR8Oo1LmBwfiKnepzcekeUqLiyExK5cSE9bg28MSmlDPpcYl859WPG98dBK0WcwcbVBampMUm6mpwqFySWCPXYFWmKE51qnJp6ibUqRmk3I3m2oKdlMl57p5Von094kLCuP/LSbRqDfd2HyP22GVKdWkIwKk+iznVayFZj1MwsbbAtJAVGTn5A9h7lyfeyPk/q2LbQB6EXOFOzutxa08I946F4fac16PSh/U5t2wPyfeyX4+jE9dTooEntqWcAXDyLkfMOcP52pVzQalSoVDkvH1qtWg1Gr1ep5dRun09Hh6/QlTOeRDxD+dB2U71CXvmPAgd/y0uOeeBdZmiFKlThXPPnAeXFvxAhWdeS78FvSjXuQEX5+54YV7OtatQqVdjjvVehDol/aXrsS5TFOc6VbnwzPEUtuB7ynd/10DtgcSEhHEv53iK3B1CzLHLlO2SXbvKypx3D80j83EKkXtC9LbVZqr5tfZQwr78Aa1ag6m9NSorc9JjH790rq/iSV0Xn6nryoLvKWegrlLtA4l9pq6o3SHEHrtMmZy6lBZmXJ69ndSoONBoubPxTzTpWdh7ls2X3N+IVmO827/YG81JyszM5PTp0xw7dowBAwYwbtw47O3t2bt3L2ZmZnz77bdMnTqVxo0bExcXx4wZM9i1axflypXj4MGDfPbZZwQFBXHz5k22bt3Kzp07cXZ2ZuvWrYwdO5Z69eoRFxfHJ598wpAhQ1i7di137tyhX79+WFhY0LFjRwB+/fVXZs6cyaxZszh06BC9e/emYcOGeHt7M336dC5cuMCuXbuws7Nj8uTJREZGAqDRaOjXrx9lypRh3759ZGZmMnr0aCZMmMD8+fMBuH//PuXKlWPmzJmkp6dz8eJFvedgyJAhODg4cODAARITE+nbt++bPKVGV8ffl6bvBWNiomLExJkFnQ4AhdxcSbgcrrfs8dVI7KuWyhNramuJlaujXnx6zGMy4pOxr1IKrRbS4xJJexCvty/rEk6Y2llhW84FpakKj8/b4lzTjczHKdzc/Bdhy/ZCTkP7ddhXKsGjMP0a4q9F4lDFcA3WxR314tNiHpORkIyDeymS7j4kKzkNgDYnF2FdrDAPjoVxY+sBAByqlEJpqsJ7eBuK1HQjMzGFa5v/5uLyN6vB1q0EGXGJpD94pFuWeCUSqxLOmNhZkfU4RS/2ca56E69GUiinXm2WGm2Wmsqj2lNhYAuyktI4/tFsXay9dznSoxNocGQ+prZWxBy5xKVJG0i7F/fa+T/L3q0Ecblfj6uROLobfj1sDLwe6QnJFHYvRWJ4DI4eZchKScez7wcoVErC/wjlxPQtZCSkEPnXeaLPXKfZrolostQoTVSETNlIzCs2Al/lPDB5wXlQqEop+IfzIPNxChdnbyf1XhzOAe7PT0qpwHdWNy4t+J6kWw9eqR47txJ5cnhyPD3J4dlYw8dTaQA06Zn8HjSC9JjH+C7sneexnjTegnZPxLGmG4+vRHBt6Z5XyvdlPTlPXqau558n2XWd/XyN3jqnOlUwtbMiIWdY+q0iE7eB1xxu8/Pzw8/Pj4CAAKZOnUq3bt346KOP+OKLL5g4cSKmpqZERUVhbW1NWloaCQkJqFQqtFotW7Zs4dSpUwQEBBAaGkqRIkUwNzcnISGB7777jkuXLtGuXTuOHj2Kqakpu3fvpnz58nTu3BlTU1MqVKjAp59+ysaNG3U5lSlThpYtW6JSqQgKCsLZ2Znbt2+TmZnJ7t27GTJkCMWKFcPa2poJEyagUqkAuHDhAhcvXmTixInY2Njg4ODAyJEj2bt3L48ePf3H0bZtW0xNTbGxsdF7LiIjIzl58iTDhw/HxsaGYsWK0b9//9d9LfKFk2NhTExUBZ2GHhNrS7JS0vSWZaWmY2JlkTfWxjJ7fa54dWpGdo+FjQVZqfqfdp/cN7G2wNTOiugjl7m2Zh+7qw/gWP9lVPy0EW598naVvwpTGwuycn3KVqdmYGqdtwZTXQ2588yu4Vk/1B3Gtur90Wo0BK0aCICZrRX3j14mbO2vbPcbyMEBy3Hv/h5VDXT3vwoTG8s8PQXqZ547/VgL1Llfg5R0VLnirn75PT+V+YSr83bgv3kUVqWKgFJB2v1HRP91loONxvJX0AjQaqm54XNQKt6ohidMrQ0dB3mfX/jn18PC0Za4C3e4tfc42+t/zo8tJmNX1oX6i/oBoDQ3IfHuQ37uOIN1Fbrz68dzqT6sDa71PF4pZxNry7zP6XPOgyc5543PztnExkL32j27r+zHyd5f6ks0SEu1roOJtcUrD7PBk2PkZY8ny7znT8rT10ur1pAe8889QwfbT+dHt54kXA6n7rYxRjue9HPNe64/OdZyH//Pqyt3HIBD9QrU/GoQYXN3kHL3oZGzFsbyyj1JEydOpHXr1gbXhYeHM3v2bG7fvk2ZMmUoXTrnU4FGQ4kSJVi/fj2rV6+mT58+qNVqWrduzYgRI/Dx8WHx4sW69RYWFnTp0oW+ffsSGRnJxYsX8fPz0z2ORqPRNXQAnJ2d9fIwNTVFo9EQHx9Pamoqrq6uunVPGkMAERERqNVqgoKC9LY3MzMjPPzpp4EiRYoYrPfBg+xPWsWLF9ctK1Uq76fA/+/cBzbHfWAL3f2409dRWZrrxZhYmpOVnJpn2yeNI5Nc8SpLMzKT0lAoFXnWPbmflZTKgwMXeHDgwtPHDr3J1a9+oVSLWlxZvvela/AY0JxqA5rr7secuYGJpf68jic5vWwNJpZmZCXp16xOyyQ1LZ5T07bQdO8UzApZce/gBe4dfFpDbOhNLq/eR+nm/lxc8fI15KZOScvzOqieee70Y9PzxlqZk5WrXk3ORPObK3+iVOcGuLzvy81VP3Os/XS9uAtj19Ho4ipsK7mSGBbxyrl79W+O1zOvx8MzNzDJNc/GxNKMzORXez0yk1JJi3nM3rZf6JYnR8VyYtoWmv84CVNrC6oPa4M6PZOoQ9m9yuF/hHJz11HcPgom8pljLbfKuc6DWAPngeofzoO88WbZr4FS8dzXMjMp7/6ep9xHDbi54U/d6/gqDB4jz8khKyUNVe7zxyrv+fBPNGmZaNIyOTfuG5peWEGhKqVIuGDcXhlDdZk89zx5ubpKd6pPtakfc3n2dm6s/Mmo+RrNv3yYzFiM9hUAmZmZ9O7dm6FDh9KpUycUCgUXLlxg9+7sKyNiY2NRq9UsXboUjUbD6dOnGThwIGXLlqVBgwY4OjqyZs0aMjIyOHr0KP3796dq1aq4uLjg7+/PmjVPuykfPXpEcnLyP+bk6OiIhYUFUVFRlCtXDoCUlBRdL5GLiwsWFhaEhIToGl0ZGRmEh4dTunRpTp06BYBCYfjTiYuLC5DdOCxfvjyQPTwn9F1etJvLi55eIVNtVDscqumPwdtVcjU4bygzIYWUqDjs3EqQcCX7n6mFcyHMC9uSEBaOQqnAvLAt5k52uk+edpVcSYmMJTMxFdf3fbFwLsSN9U+vPFKZm6JOy8jzWC9yYfFuLjxzlY/3yHY4epTRi7Gv6ErsuVt5ts1ISCH5Xhz2bq7EP1uDgy2PrkTg7FeR2vN68uM7o9FkZl9RojIzRZ2eSVZKOiUbZddw7Zmrp5TmJror315XYlgEZo62mDkVIiMmAQBbN1dSI2PJSkzNFRtOoVzzJmwruRIfmv2a1flxMjdX7uXenuNPczQzJSM+GYvihSnXqwlXZm/T9TQozUwB9K52exVnl+zm7JKnr4fv5+1wrFZGL8a+kisxZ1/welRy5VHO62HpXAiLnNfDwb0k5VvW5uSMrbptVGYmaDVa1JlZ2BR3JD1e//1Hk6lGk/Hiq4HCFu0m7JnzwGNUO+wNnAeP/uE8eJyTs/lLnge5X8vnMXeyw6lGJU4MXPFS8bklhEVg7qifg62b4Rweh0Vg71lGb1n28ZT39crNqqQTgTvG8fcHk3RX9inNsv+VZcT/8/+FV/XYmHUpFXjN7E7xpjUI6Tqfhwef36gucDLcBhjxG7czMzNJS0vDwsIChUJBVFQUc+bM0a2Lioqie/fuHD16FKVSSdGiRQFwcHDg/Pnz9OjRg7CwMMzMzHB0dNSta9asGaGhoezevZusrCzdROmZM/95fo1SqaRt27YsXryYBw8ekJqayowZM1Crs9/MPD09KV26NDNnziQ5OZm0tDSmT59O165ddTEvUrx4cerWrcuMGTNISEjg4cOHcpXfS7i9/RDOAe6UbOaPQqWkZDN/nAPcub39kMH4W1v/psrglliXdMbE2gKfKV2IPnKJ5DvRJN16wMOQMHymdMHE2gLrks5UHdKSm5v/yt5YocB78kcUqVsVAEffClTs0Uiv0fQ6bm4/RNEAd0rn1FC6mT9FA9y5scNwDTe2HqDawJbY5NRQY/JH3D9ymaQ70Ty6dBcTSzOqj+mI0lSFtasjvuM/5PqWv7MbTQoFNSZ1xiWnBiffCrh/2oirr3HJ+bOSb90n9lgYHlM/RmVtgWUpZyoOac3dzXmvmovYfgjHgCoUa14LhUpJsea1cAyoQsT2gwDEn76O24h2WJZwQmlmQqURbVGamfBg3yky4hJxbVWbyqM7oDQ3xaywLdVmdOPhgfOk3Il+oxqeuL7jEMUC3Cn7QfbrUfYDf4oFuHP9Oa/H1a0H8B6U/XqYWltQa9JH3Dt6mcQ70aTHJ1Ol67tU69sUhUqJdXFHao77kGvbDqLJyOLub6cp19wf16BqALjUqkz51nW48f2rXbRxZ/shigS4UyLnGCrRzJ8iAe7cec55cDvnPLDKOYa8DZwH3jnngVVJZ6oMacmtJ+fBS3CqWYnU+/Ekv+bQT/Kt+8QcC8Nz6sfZOZRypvKQVtwxkMPd7YdwDqiCa/Ps2l2b++McUIW7OcfTi6SEx5DxKJlqU7qgsjLHrLAt3jO7c39/KKkRMa+V+8vUVe2ZutyGtOKugbrCtx/CKaAKxXPqKt7cH6dn6qo2pQtFg734q9G4t7uBJHSM1pNkZWXF9OnTWbhwIV988QWOjo60b9+e69evc/XqVRo1asSECROYNGkS0dHR2Nra0qlTJxo3boxCoeD27dv07duXR48e4ejoyJgxY/Dy8gJg9erVzJ07ly+++AKVSkX9+vUZO3bsS+U1bNgwpk6dSpMmTbC2tqZDhw4olUpMTU0xMTFh5cqVzJo1i/fee4/09HQ8PT35+uuvMTc3/+edA/PmzWPy5Mk0aNAAGxsbWrduzdmzZ1/7efz/IPH6PQ53n4/n2A+pMb8nKRExHOnxJUk3s3vhSreuje/s7Ev1AS7O/x6liYrgHyZgYmNB9JFLHOm1WLe/wz0W4ju9Kx8c/xKtRsPtbYe4tOB7ACJ/PknoxA34zuiKVfHCpEUncHHODu7seLOrEB/fuMdfny6g+tiO1J7bg6SIGP7utZDEnBrKtqpNrVnd2Vwp+xLtswuya2j0/XhMrS24f+QSB/pk15CVks7vnWdTY3IX2oUuIzMxhZs7D3Puyx8ACP/lJCcmbcR/elesijmQFp3A2Xk7ubXzza+kPNnjS6rN6Mo7xxeh1WqI2HaQq/N3AtD4xtecG7GayJ2HSboexYlu83Af9yHe83uREhHDyR4LSM6p9/K0zWg1GurumYLS1IRHp69xtO0XZCZkf7I/1nEmVSd9xLtnlwEQ/dsZQge/Xo+FIQk37vH7pwuoMaYjdef2IDkihv29FvL4VnZ+5VvVps7M7nzrlv16nPnye5SmKj7Ymf163DtyiT9yXo+Ue3H8+slc/Ea1x3tgS9TpGdzcfYwT07YAcHXL35hYmlFrysdYFbEnOTKGI2O+fu53Mj3Ps+eBn4HzoFTOefB9znlwKec8aPDDBExzzoNjz5wHR3ssxGd6V5rknAd3njkPXoZ1qSKk3n+zifQhPb7Ea0Y3Gh1fiFar4e62g1zOOZ6a31jLmRFrCM85no52m4/HuI5Uzzmejj1T+z851nUenlM/5v2Ti9CkZxL1y0kuTt/6zxu+phM9vsRzRjfePb4QcuoKy6nrgxtrCR2xhoicukK6zafquI74zO9FakQMx3t8SfLN+5gVtqVct/fQqjU0/Hu23v6fbP820Wrle5IAFFrtG1we8y9w4sQJ3NzcsLOzAyApKQlfX1/27dtHmTJlCja5Z2TG5N/l0f8rO6uNL+gUjCI9HyZ/FgSHl+gNfdtFv2UXHbwuO/W//21W9R/5V/Ff+crjlvc35ev+00KNd7WghfcHRtvX/9p/5Xh5rrVr1zJt2jTS0tJIT09n0aJFlC1b9q1qIAkhhBDi7fOfbyRNmjSJxMREgoKCqFOnDnfu3DH4pZBCCCGEyKHRGO/2L/af/4HbokWLsmzZsoJOQwghhPj3kK8AAP4fNJKEEEII8YrkB26B/wfDbUIIIYQQr0N6koQQQgihT4bbAGkkCSGEECK3f/mEa2OR4TYhhBBCCAOkJ0kIIYQQ+mS4DZBGkhBCCCFyk+E2QIbbhBBCCCEMkkaSEEIIIfQV0Ddux8bG0q9fP/z8/PD392fatGlkZWUZjP3mm28IDg6mevXqNGvWjH379hmjcj3SSBJCCCGEHq1WbbTbqxg8eDBWVlYcPHiQ7du3c/ToUdatW5cn7u+//2blypWsXr2a06dP079/fwYPHkxERISRnoFs0kgSQgghRIG7c+cOx48fZ8SIEVhaWlKyZEn69evHxo0b88TevHkTrVaru6lUKkxNTTExMe5Ua5m4LYQQQgh9Rpy4nZGRQUZGht4yMzMzzMzM9JZdu3YNe3t7ihYtqltWvnx5oqKiePz4MXZ2drrlTZs2ZefOnTRp0gSVSoVCoWDOnDm4uLgYLW+QniQhhBBC5KbVGO22cuVKfH199W4rV67M85DJyclYWlrqLXtyPyUlRW95ZmYmlStXZtu2bYSGhjJlyhTGjh3LlStXjPo0SE+SEEIIIfQZsSepd+/edOvWTW9Z7l4kACsrK1JTU/WWPblvbW2tt3zq1KlUr14dT09PANq0acOePXv4/vvvGTVqlNFyl54kIYQQQuQbMzMzbGxs9G6GGkkVK1YkPj6emJgY3bIbN27g4uKCra2tXmxUVFSeITwTExNMTU2Nmrv0JL0ldlYbX9ApvLHW56cWdApG8ZHv0IJOwSjex6agU3hjptqCzsA4HDWZBZ3CGyvvGlfQKRiFRaF//2vxP1EA37hdpkwZfH19mT59OlOmTOHRo0csW7aMtm3b5okNDg5mw4YNNGjQAHd3d3799VdCQkIYOtS479/SSBJCCCGEvgL6xu1FixYxZcoUGjZsiFKppGXLlvTr1w8AHx8fJk+eTPPmzenfvz8qlYoBAwaQkJBA6dKlWbp0Ke7u7kbNRxpJQgghhHgrODk5sWjRIoPrzpw5o/vbxMSEAQMGMGDAgHzNRxpJQgghhNAnP3ALSCNJCCGEELnJD9wCcnWbEEIIIYRB0pMkhBBCCH3SkwRII0kIIYQQucmcJECG24QQQgghDJKeJCGEEELok+E2QBpJQgghhMhNhtsAaSQJIYQQIjfpSQJkTpIQQgghhEHSkySEEEIIfTLcBkgjSQghhBC5yXAbIMNtQgghhBAGSU+SEEIIIfRJTxIgjSQhhBBC5KbVFnQGb4W3qpGUkJDAggUL+PPPP0lISMDGxoY6deowZMgQXFxcjPpYK1as4OTJk6xevdqo+wVwc3Pj22+/xd/f3+j7Lhbshee4D7Ep7UxKZCyhUzZz7/czBmMVSgWeYztSpl0gKkszog9d4uTItaRFxwNg7miH39xPKRLgjlat4c6OQ4RO3oRWnf0JwndmN8p2DEKTpdbtM3TSBm5u+NPodb2suEfxdO49lMmjBlOzumeB5WGIdwNfOo/6mCKlihIb9ZAN077h9B8nDcY6uTrTfUov3Gq4o0DBpWMX+GbqGh6GR+vFKZRKxm+awsOIaJYPX5QveZcI9sJvTEdsSjuTHBnLiS82E/F7qMFYhVKB75iOVGhbFxNLM+4dvsSRUV+TmnNMlW3uT73F/VCnZ+q2ufPLSQ4OXKG3H2ffCrz/3RjWl+9utDpcg72o/kwdp77YTOQL6qg+piPlcuq4f/gSx56pw6VOFXxGtadQRVfUqenc2XOcU9M2o07Lrst/RlcqdNA/N05O3si1ja9/bpg62VF5bm/sa1dBm6XmwY6DXJ+0Xnc+PsuxoQ/lx3fGsnQR0iJiuD5lA7G/nQZAaWFKxSldcWpcA6WZCYnnb3FtwjckX7oLgEVJZypM/hh7f3dQQMLxK1ybsI60uw9fO/fnUTrY4zhuCBZ+Xmiz1CT//DuPFqwEAzXZtPkAu85tUDk7oo6J4/GmnSRt2/10fdtm2HVpi8qxMFmR94lfsprUgyFGz9kQhb09dsOGY+btDWo1qb/9RtLy5aBR5wpUYP3xJ1g2aYrC1gb1vfskr/+W9L9yjgtTM2x69cIiKAiFpRXq8LskrlpFZqjh93BR8N6qOUlDhgzh0aNHbN++ndDQUH744QcyMjLo1q0bWVlZRn2sPn365EsDKT/ZlC1K7dWDuTB7Gzsr9eTCnB3UXjUASxcHg/FVBrfCJagav74/jt0+A1CnZVBjXg/d+oCVA8hKTmO3T39+azyeooEeVOrVWLe+sHc5To5Yw84Kn+puBdlAOn3uIp17DyU88l6B5fA8LmWKMWzF52ydt4luHp34bv4WBi8bgUPRwgbjh60cRdz9WPrU6E6fmt1ITU6l39yBeeLaDu6Ae033fMvbrmxRGqwaxOk529lYuRdn5u2kwYoBWD3nmPIa1BLXIA9+bDKerb4DyUrLoM7cp8eUk1c5buw4zIZKPXS33A2kih3q0WjTSEwszIxWh23ZogStGkTonO1sqdyLs/N2ErTi+edGtUEtKRbkwd4m49meU0dATh3mhW0J/mY4V9fvZ4t7L/Y0GkfRAHc8Pmum297RqxxHR65lc6UeutubNJAAPFYNRp2cxmGv3pxsPAaHetUo2btpnjjLsi54rBnGzVlbOVChK7fmbMNj1RDMcmotO7w9VuWLERI4hEMePUm6eAfPr4c/rX3dCLLikzhS4zOO+n1G5qNEPL8d+Ua5P4/zzHFoUlOJaNSB+x/3x6Jmdew6t81bU/3a2A/4lJiJswkPbE7MxFnYf9YNq+BAAKw/eBf7Xl2IGTOd8LrNSFi7Cec5E1E5OeZL3rnZT5iENjWVh23bENe3D+a+vli1a5e3jpatsHivEY+GDOJhk8YkfbWKQuPGoypeHACbXr0w86hG3Gf9eNiiGal79+IwfQbKIkX+J3W8Eo3GeLd/sbeqkXTq1CneffddnJ2dAXBycmLMmDF4eXnx+PFjgoOD2blzpy4+JCQENzc3ACIiInBzc2PmzJnUqFGDMWPG4OPjw6FDh3Txjx8/xtPTk3PnzrF48WK6dOmCRqMhODiYrVu36uLUajWBgYH8/PPPABw5coS2bdvi5+dH06ZN2b376aebzMxMZsyYgb+/P7Vq1crXhleZ9vWIOX6FyF9OoVVrCP8xhIdHwyj3UbDB+HKd6nN56R5So+LISkrl9PhvKRbshXUpZ2zKFKVonSqcnboZdWoGyXcfcnHBD1Ts/h4ASjMTClUuSdzZm/lWz6vY9dNvjJw0m4G9PinoVAwKatuAy8cvc/LXEDRqDcf2HuZSyAXe6fSewfgJbUbx9cSvyEzPwNLWCktrSx7HPdaLqVq7Gv6NAwj5+Vi+5V2hXSAPjl/h7r7sY+r2jyHcPxpGpc4NDMZX6lSf80v3kBwVR2ZSKiET1lOigSc2pXLOWa9yxLzgmKk7vxeVOjfgzLydz415HeXbBRJ9/ArhOXXc+TGEBy+oo2Kn+lxcuoeUnDpOTFiPa04d6XGJfOfVjxvfHQStFnMHG1QWpqTFJgLZ54ZD5ZLEGvHcsCxTFIc6HlyfsgFNagZpd6K5PX8HJbq/nye2WIcgEkIuE/PzCbRqDdG7jxJ/9BKuXd4BwKqSKygUuptWrUGdmgGASSFrMqLjuTlzK5qUdNQp6YR/9RM27qUwKWRttHoATEoWx6KGN/FffoU2LZ2syHskrN6IbYcWeWJVzk48/noLGecvA5Bx7jLpJ0Mxr14NALsu7Ylfvo6Mi1cASNn3J/e7DkSTnGzUnA1RFXfFzMeHpJUrID0d9b17JK3/FquWrfLEpv7wPbGfdkMdFQWmpijt7dGmpaFNSwNAYW5O0tdr0Tx8CBoNqXv3oM3MxLSSW77X8cqkkQS8ZcNtTZs2ZeLEiZw8eZKaNWvi5eWFq6srM2fOfOl9JCcnc/jwYdJyDsrvv/+eunXrArBnzx5Kly6Np6cnf//9NwBKpZI2bdrw/fff06FDBwAOHTpERkYGDRs2JCwsjL59+zJnzhwaNmzI2bNn6devHw4ODgQGBrJs2TL++usvtm/fjqOjI5MmTTLuk/KMQm6uJFwO11v2+Gok9lVL5Yk1tbXEytVRLz495jEZ8cnYVymFVgvpcYmkPYjX25d1CSdM7aywLeeC0lSFx+dtca7pRubjFG5u/ouwZXsLZKy6jr8vTd8LxsRExYiJL388/K+UqFiK8Ct39JZFXougtHtZg/GZOcNRAxYOoXbzQOKjHzG10wTdejvHQvSZ3Z+5PWfQ5NPm+Za3faUSPArTP6bir0VSuIrhY8q6uKNefFrMY9ITkinsXoqk8Bgcq5UhKyWdav0+QKFSErE/lJPTt5CRkALA6TnbSbkXh0uAcXvHnleHwyvUkZGQjIN7KZLuPiQrOfv9o83JRVgXK8yDY2Hc2HoAAIcqpVCaqvAe3oYiNd3ITEzh2ua/ubj89c8N68olyYxLJOPBI92y5KsRWJR0xsTOiqzHKU9j3UqSdPmu3vbJVyOwqVoGgPDlP+KxZhj1wtaiyVKTGZfImdaTAchKSObsh9P1ti3yQS1S70aTlWDcBodpuTKo4x+jjonVLcu8eQeTYkVR2FijTXr6eM8Oq0H2MJ25jycp81egsDDHtHxptBoNRVfPx7RcGTLvhBO/6Cu0qWlGzdkQk7Jl0CQkoIl9Wof69h1ULi4orG3QJic9DdZqIS0NMz8/7GfOBoWCxGVL0MTFAZA4f57evk19fFBYW5N5/Vq+1yFez1vVk/TFF18wYcIE7t27x4QJEwgODubdd9/V67n5Jy1btsTMzAw7OzvatWvH/v37SUrKPoi///572rbN29Xbtm1bzp07x927d3VxLVq0wMzMjC1bttCwYUPee+89VCoV1atXp3379mzcuBGAXbt28emnn1KyZEmsrKwYN24cCoXCCM9GXibWlmSl6L8pZKWmY2JlkTfWxjJ7fa54dWoGJtYWmNpYkJWanmdf2Y9jgamdFdFHLnNtzT52Vx/Asf7LqPhpI9z6NDFmSS/NybEwJiaqAnnsl2FpY0laruc6PTUdC+u8r82zVny+lK5VPuTonsNM3PIFlrZWKBQK+n85hL2rd3Pn8u18zJrs4yAl93GQgamBvE1zjqnMXPFPjikLR1tiL9zh9t7j7Az6nL3NJ2NXzoV6i/vpYlPuxeVDFYbrUP9DHYbqNskV/0PdYWyr3h+tRkPQquzhUDNbK+4fvUzY2l/Z7jeQgwOW4979Par2fv1zQ2VtiTpXPpqUjJx1+jmpbCzyxKpTM1BZmwOgMFHxcG8Ih7x6c7BSN2J+PoHnNyNQmpvmedziH79Lqb7NCBu28rVzfx6ltWWeRsyTHhWlleXzt3N0oMiS6WSEXSP5l/0obW1QKJXYdWlH3PSFRLzXnpRf/qDI4hmoihU1et65KSytdHk/oU3P6RmyNFxHxtmzRL/3LvEjhmHTvQfmDfL2aJq6V8F+4mSSv1mH5v594yf+prQa493+xd6qniSlUkmLFi1o0aIFWq2WGzdusGvXLj7//HPdENw/KfLM2K6Pjw8lSpRg3759eHt7ExYWxldffZVnm6JFixIYGMgPP/xA165d+eOPP9ixYwcAkZGRHDt2DD8/P128Wq2mVKnsT6jR0dEUK1ZMt87Ozo5ChQq9Vv25uQ9sjvvAp13Tcaevo7I014sxsTQnKzk1z7ZPGkcmueJVlmZkJqWhUCryrHtyPysplQcHLvDgwIWnjx16k6tf/UKpFrW4snzvmxX2H9Dys7a0+qyN7v610GuY53o+zS3NSU3K+9o8KzM9+x/h+mnrCO74Lh61q1GiYkky0zP4ZZ3xn2fPAc3xHPC0Z+rhmRuYWOrPDTLJOUZye9ExlZWUSlrMY35u84VueXJULCe/2MIHeyZhYm2h650xBo8Bzan2TB0xBupQvWIdJjl1PEudlklqWjynpm2h6d4pmBWy4t7BC9w7+PTciA29yeXV+yjd3J+LK17vNVOnpKHMlb/SKvt+npxS0vO8D6gszVAnpaEwUeHx1VDOdp5Bxv3sXqmrY9ZS79rXOAR5EvvrKQAUpioqTulK0Za1OfvRTOIPX3ytvF9Ek5qGwkI/T4VFdoNPk2L4vDCr5o7zrPGkn7lAzKQ5oNagzczudU3csJ3Mm9m9tYlbd2HTthmWdf3z9EIZmzbNQB3m2XVoU1MMbQI5OWecPk3ab79i0fAd0v98OmfNsklTbPr3J/nrr0nZ9l3+JP6m/uXDZMby1jSSDh48yMCBA/nzzz+xt7dHoVBQoUIFhg0bxuHDh7l06RJKpZLMzKdXzTx69CjPfnL34rRt25Y9e/Zw584d3nnnHezt7Q0+frt27Zg9ezZFihShcuXKVKxYEQAXFxdatWrFlClTdLHR0dFoc7rVXVxcCA9/2m2fkpJCYmLiaz8Pz7q8aDeXFz19A6g2qh0O1fSHb+wquRqcN5SZkEJKVBx2biVIuBIBgIVzIcwL25IQFo5CqcC8sC3mTnakxzzW7SslMpbMxFRc3/fFwrkQN9b/odunytwUdVqGUWr7t/th6XZ+WLpdd7/DiM6U9SinF+NasQQ3z93Is62puRmzf17AkiFfcuNsdje7UqVEqVKSFJ9EYKv6OBQtzNpz2b2V5jn/PGu85093z85vlPe5xbs5t/jpMVV9ZDscPcroxdhXdCXm3K0822YkpJB8Lw4HN1fic44pS+dCWDjY8uhKBA7uJSnXsjanZjyd36c0M0Gr0aLJNO6FFxcW7+bCM3V4P6eO2BfUYf9MHRbOhTDPqcPZryK15/Xkx3dGo8nMvnpJZWaKOj2TrJR0SjbKPjeubXh6bijNTXRXvr2O5LBwzBztMHUuRObDBACsK5UgLTIGdWJqnljbXO8D1pVK8PjsTVTWFpg62KA0e/rWrlVr0Gq0aDOyXwPTwrZ4rh+J0syEE41G5ctVbQCZ12+jciiEsrA9mrj47McuV5qs+9F6Q226Glq8T+HPPyN++Tckbnh6bmniH6OOfQRm+j1hCqUS8qfTXk/WrZsoC9mjdHBAk/M/R1WmNOroaLS55kTZ9M3uNU1avuxpnqamaB/n/E9QKrEdPASLwHokjBtHxulT+V/A65KvAADeouG2GjVq4OjoyOjRo7ly5QqZmZkkJSWxe/dubt++Tf369Slfvjz79+8nLS2Nhw8f8u233/7jflu2bKm7Uq6dgasRnqhfvz4pKSmsWrVKL+5JI+vQoUNoNBpu377NRx99xNq1a4HsxtXq1au5ceMG6enpzJw5E7Va/byHeSO3tx/COcCdks38UaiUlGzmj3OAO7e3HzIYf2vr31QZ3BLrks6YWFvgM6UL0UcukXwnmqRbD3gYEobPlC6YWFtgXdKZqkNacnPzX9kbKxR4T/6IInWrAuDoW4GKPRrpNZrEUwd3/kXVWh7UaloHpUpJraZ1qFrLg4M7/8oTm5meQcS1cDqP/gRbB1vMrSz4dGov7t2M4urpKwxt2J9uHp3o7tmZ7p6dObTrIId2HXzjBpIhN7YfwiXAnTI5x1SZZv64BLhzY4fhY+ra1gN4DWyJTc4xVXPyR9w7cpnEO9Gkxyfj3u1dPPo2RaFSYl3ckRrjP+T6dwfRZBi3kZTbze2HKBrgTumcOko386foC+q4sfUA1Z6po8bkj7h/5DJJd6J5dOkuJpZmVB/TEaWpCmtXR3zHf8j1LX9nN5oUCmpM6oxLzrnh5FsB908bcXXD658bqbfuE3/sMpWmdkVlbYFFKWfKDG3DvU15r5i7v+0A9rWrUqR5AAqVkiLNA7CvXZX72w6QlZBM/LHLlB/fGVMnO5TmppQf35nMuETij4ehMFHhtWUsWY9TONVsfL41kACywiNJO3OewsP7obCyxKS4C4V6dCZp1y95Yq2CA3EcPZCHwyfrNZCeSNzxI/Y9u2BaqTyolNh2bImqiBOpfx7Ot/yfUEdGknHuHLafDUBhaYnSxQWbLh+T+lPeXsPMc2exatYcU09PUCgwC6iNRYNgUvf+CIDtZ/0xr+lPbJ9eb3cDSegotNq3p7kYHR3NkiVLOHToELGxsZiamuLt7c2AAQPw8vLi2rVrTJo0ibCwMIoUKcInn3zCxIkTuXLlChERETRs2JD9+/dTokQJvf0OGzaMM2fOsH//fl1P0+LFizl+/Djr16/Xxc2fP5/169dz8OBBbGxsdMv/+usvFi1axJ07d7C0tOSDDz5g6NChmJmZodFoWLRoEVu3biUrK4v27duzfft2Fi1a9Erfk7S12Mv9A3SpXw3PsR9iU6YIKRExnJ26mXt/nAWgdOva+M7OvlQfsucmVPu8LaXb1MXExoLoI5c4OXwN6bHZPUfmTnb4Tu9KkTpV0Go03N52iHNfbEaryT4kyncJplKvxlgVL0xadAJXVvzE9W9+f25urc9Pfel634RHncasXTwr374n6SPfoa+1nVc9bzqN+oSipV2IiYxmw4xvCf0z+42wbst69Jzel0+qfAiAlZ01XcZ1w7ehH1otXDhyjvVffE18dN7e0b45Xw3wqt+T9H6WzT8HAcWDquE3tiN2pYuQFBHDyWlbiMg5psq1qk3tWd3ZUCn78niFiYrqI9pSvk0dTK0tuHfkEkc+X0tazjFVtFZl/Ea3x96tJOr0DG7tOsbJaVv0vjcJwCXAncbbx/K160cvzO1VZqEVD6pG9bEdsc2p4/S0LUTm1FG2VW1qzerO5mfq8BnRlrI5ddw/coljz9RRqGJxakzugqNXOTITU7i58zDnvvxB19ir+FEwVXo1xqqYA2nRCVxa9TNXXnBulMj65x5YU+dCuE3vjn2dqqDVcv+7A1yfugE0Wurd/JYrI1bxIKfRV7i+V/b3JJUpSlrEQ25M2Ujs/jO6/VSY8BGF63uhNFGRcOoa1yZ8Q+rNezg3qUm1r4ejTs1Am2s4JSRwCOmRsXnyeqK866vPJ1MWtqfwyAFY+HmDVkPSnt+IX7QaNBpKHvqRuGkLSP75D4ptXYVpudJo0/Wfp+Sffidu+kJQKLDt3Abb1k1RFXEi89ZdHs1fTvqZC4Yf+AUsCr16j5/SwQHbgYMx8/EGjZbUX/eRtGolaDQ4//QzifPnkfZ79utv0bgJ1h92QunggDoigqS1q8k4cQKFXSGcd34PGg3aLP0cnt3+ZRX98+9XruNVpH79udH2ZdltttH29b/2VjWS/j972UbS2+x/1UjKb6/bSHrbvGwj6W329k7VfzUv00h6271OI+lt9DqNpLdRvjeS1gz/56CXZPnpXKPt63/trRluE0IIIYR4m7w1E7eFEEII8Zb4l1+6byzSSBJCCCGEnidzU/+/k+E2IYQQQggDpCdJCCGEEPrkyyQBaSQJIYQQIjeZkwTIcJsQQgghhEHSkySEEEIIfTJxG5CeJCGEEELkptEY7/YKYmNj6devH35+fvj7+zNt2jSysgz/rNHx48dp164dPj4+BAUFsXLlSmNUrkcaSUIIIYTQV0CNpMGDB2NlZcXBgwfZvn07R48eZd26dXnibty4Qa9evejUqROnT59m5cqVrF27ll9+yfvbgG9ChtuEEEIIkW8yMjLIyND/aR4zMzPMzMz0lt25c4fjx49z4MABLC0tKVmyJP369WPOnDn06NFDL3bTpk00bNiQVq1aAVC5cmW2bNmi97urxiA9SUIIIYTQp9Ua7bZy5Up8fX31boaGxq5du4a9vT1FixbVLStfvjxRUVE8fvxYL/bcuXOUKFGCoUOH4u/vT+PGjTl+/DjOzs5GfRqkJ0kIIYQQ+oz4PUm9+/WmW7duesty9yIBJCcnY2lpqbfsyf2UlBTs7Ox0yxMSEvj2229ZsGABs2fP5syZM/Tu3ZtChQrx/vvvGy136UkSQgghRL4xMzPDxsZG72aokWRlZUVqaqresif3ra2t8+yzYcOG1K9fHxMTE2rUqEGLFi34+eefjZq7NJKEEEIIoU+jNd7tJVWsWJH4+HhiYmJ0y27cuIGLiwu2trZ6seXLl88zz0mtVqPVGverC6SRJIQQQgh9Wo3xbi+pTJky+Pr6Mn36dJKSkggPD2fZsmW0bds2T2zHjh3Zv38/u3btQqvVcuLECX788UdatGhhzGdBGklCCCGEeDssWrSIrKwsGjZsSPv27QkMDKRfv34A+Pj4sHv3bgACAgJYtmwZ3377Lb6+vowePZqRI0fSsGFDo+YjE7eFEEIIoa+AvnHbycmJRYsWGVx35swZvftBQUEEBQXlaz7SSHpLpCsVBZ3CG/vId2hBp2AUG07NL+gUjKK5z2cFncIbG5Fh+89B/wKPFf/+t9pGEQkFnYJRXL8QVdApGIXh76A2Hq0Rr277N5PhNiGEEEIIA/79H2+EEEIIYVzyA7eANJKEEEIIkdsrXJX2XyaNJCGEEELok54kQOYkCSGEEEIYJD1JQgghhNAnV7cB0kgSQgghRG4y3AbIcJsQQgghhEHSkySEEEIIfXJ1GyCNJCGEEELkJsNtgAy3CSGEEEIYJD1JQgghhNAjv92WTRpJQgghhNAnw22ADLcJIYQQQhgkPUlCCCGE0Cc9SYA0koQQQgiRm3wFACCNJCGEEELkJj1JwH+gkRQcHMzDhw8xMckuRavVYmNjQ7NmzRgxYgRK5fOnXQUHB9O/f39at279v0r3jbkGe1F9TEdsSjuTHBnLqS82E/l7qMFYhVJB9TEdKde2LiaWZtw/fIljo74mNToeAJc6VfAZ1Z5CFV1Rp6ZzZ89xTk3bjDotEwD/GV2p0CEITZZat8+TkzdybeOfRq/Lu4EvnUd9TJFSRYmNesiGad9w+o+TBmOdXJ3pPqUXbjXcUaDg0rELfDN1DQ/Do3PVr2T8pik8jIhm+fBFRs/5dcU9iqdz76FMHjWYmtU9Czqdf1SjQQ26jelGsVLFiI6MZs20NRzff/wft/t4xMcEtwqma+2u+ZqfqZMdlef2xr52FbRZah7sOMj1SevRqvN+EnZs6EP58Z2xLF2EtIgYrk/ZQOxvpwFQWphScUpXnBrXQGlmQuL5W1yb8A3Jl+4CYFO1NBWnfIKtZzk0mVnE/RHK1fHryHqUZJQ6zJzs8JnTA6fa7miyNETsOMSFyRsN1lG0oTdVxnXEunQRUiNiuTB1Ew9+O5MnrtrUjzG1s+T0oJW6ZValiuA5oyuFq1dAq1bz4M9znB/7DZmPU4xShyH1GtZm+PgBlCjtyr3I+8yZvIi/fjtkMNbRuTCHL+4jOflpPvGx8TT0awFA4xbvMmf5FNLTM3Trf//pL0Z+NjHf8gdo/H4w06ePoVzZ0twNj2TUqC/Y+9PveeJKlizO+bN/6S1TqZRYWlpSN7A5x0JO0aB+HaZ9MYrKlSuSkpLK9h17GDV6Gmlpaflag3h1/4mJ25MnT+bMmTOcOXOG0NBQ1qxZww8//MCSJUsKOjWjsi1blKBVgwids50tlXtxdt5OglYMwNLFwWB8tUEtKRbkwd4m49nuO5CstAwC5vYAwLywLcHfDOfq+v1sce/FnkbjKBrgjsdnzXTbO3qV4+jItWyu1EN3y48GkkuZYgxb8Tlb522im0cnvpu/hcHLRuBQtLDB+GErRxF3P5Y+NbrTp2Y3UpNT6Td3YJ64toM74F7T3ej5vonT5y7SufdQwiPvFXQqL6V4meKMXTWW9XPX06ZKGzbM38Do5aNxdHF84Xbedbxp26ft/yRHj1WDUSencdirNycbj8GhXjVK9m6aJ86yrAsea4Zxc9ZWDlToyq052/BYNQSznPOn7PD2WJUvRkjgEA559CTp4h08vx4OgMJUhdem0Tw6dJGDlbtzrNZAzIraU3HyJ0aro8bKAWQlp/GL92f83Xg8zvU8KN+7SZ4467Iu1Fw9mLBZ29lbsQeX526nxsqBWDzzPmDqYIPvkn6U7/l+nu39VvQn8UoEP1fry+91h2NVwgmPSZ2NVkdupcuWZNHaWSyctYIaFRqwePYqFnw1gyIuzgbjq3lXIeJOJL5lg3S3Jw0kgGo+Vdi97We99fndQKpQoSzfbV3FxElzKOxUmclT5rF50wqKF3fJExseHoV94Uq6m6OzO0ePnmLN2k0cCzmFk1Nhdu/6hhUr1+Po7I5fzUYEBQUw8vPP8rWGV6XVaI12+zf7TzSScnNzc6NGjRpcunSJlJQUpkyZQkBAAH5+fvTs2ZPIyMg82zx48IDBgwcTHByMl5cXDRs2ZPv27br1mzZt4p133sHPz49mzZqxbds23brFixcTFBREzZo1adOmDfv378+Xusq3CyT6+BXC951Cq9Zw58cQHhwNo1LnBgbjK3aqz8Wle0iJiiMzKZUTE9bj2sATm1LOpMcl8p1XP258dxC0WswdbFBZmJIWmwiA0swEh8oliT17M19qeVZQ2wZcPn6Zk7+GoFFrOLb3MJdCLvBOp/cMxk9oM4qvJ35FZnoGlrZWWFpb8jjusV5M1drV8G8cQMjPx/I9/5e166ffGDlpNgN7Ge8fa357p907XDx+kaP7jqJRazi45yDnj52ncafGz93G3smegbMGsmvtrnzPz7JMURzqeHB9ygY0qRmk3Ynm9vwdlOiet3FQrEMQCSGXifn5BFq1hujdR4k/egnXLu8AYFXJFRQK3U2r1qBOze6t0GaqORowiNtf7kSr1mBib43KyoLM2Md5Hud1WJcpinOdqlycugl1agYpd6O5suB7ynV/N09sqfaBxIaEce+Xk2jVGqJ2hxB77DJlugQDoLIy551D88h8nELknpA829tWLI5CqUChzKlT87TO/NCyQ1NOhYSy/+e/UavV/LL7d04cPU2Hj1sZjK/mU4ULZy8/d3/VvF+8Pj983KUdhw4dZ/fufajVarZv/5EDB47Ss8c/Ny7HjhlEkaJODBg4FoCYmDiKuXrx7frv0Gq1ODo6YGFuwcOHsfldxqvRaI13+xf7zzWSMjMzCQkJ4dixY9SpU4cpU6Zw/vx5du7cyZEjR3BycmLo0KF5ths3bhympqbs3buX06dP89FHHzF16lSSk5MJDw9nxowZrFq1ipMnT/L5558zdepUoqOjOXbsGFu3bmXbtm2EhITQrl07xo4dS2ZmptFrs69Ugkdh4XrL4q9F4lClVJ5YU1tLrIs76sWnxTwmIyEZB/fs+Kzk7K7dNicX0fyPmaQ+iOfG1gMAOFQphdJUhffwNrQLXUrLg3Oo2u+D7H8gRlaiYinCr9zRWxZ5LYLS7mUNxmemZ6LOUjNg4RBWHF9Lea8KbJ27UbfezrEQfWb3Z/HA+aSnphs939dVx9+Xn79bS+N3ggo6lZdWulJpboXd0lt299pdylYx/NooFAo+X/Q525Zv487VOwZjjMm6ckky4xLJePBItyz5agQWJZ0xsbPSj3UrSdLlu3rLkq9GYFO1DADhy3/EunJJ6oWtJejWelza1eNCzwW6WE1KOmi1VP9xCrVPLEVlY8mdZbuNUoetWwky4hJJexCvW5Z4JRKrEs6Y5qrD1q0Ej3O9DyRejaRQldLZeaZn8kfQCM6NWYc6Oe/wTdjcnZTr3ogPbn5N08urUJmbcfGLzUapw5AKlctx9fINvWU3rt7CrWpFg/Ee3lVwKV6U3X9v4cilX1m56UvKV8o+3hQKBVU83Qh6pw77T+3mr9A9TJk7BrtCtvmWP0CVKpW4cCFMb9nly9fw9Kzywu3KlSvN5yM+o3fvEWRkPG2IJiUlA3D75knOnvmD+/cfsO6brcZPXLyx/0QjafLkyfj5+eHn50dAQABTp06lW7dudOjQgb179zJo0CCKFSuGmZkZo0ePZty4cXn28cUXXzBx4kRMTU2JiorC2tqatLQ0EhISUKlUaLVatmzZwqlTpwgICCA0NJQiRYpgbm5OQkIC3333HZcuXaJdu3YcPXoUU1NTo9dpamNBVor+P311agam1hYGYi0B8sRnpWZgkiv+h7rD2Fa9P1qNhqBV2cNWZrZW3D96mbC1v7LdbyAHByzHvft7VDXQ/f+mLG0sSUvRfzNPT03HwkBdz1rx+VK6VvmQo3sOM3HLF1jaWqFQKOj/5RD2rt7Nncu3jZ7rm3ByLIyJiaqg03glltaWpOc6htJT07G0sjQY33FgR5ITk/l548//i/RQWVuizpWfJiUjZ53+8aOyscgTq07NQGVtDoDCRMXDvSEc8urNwUrdiPn5BJ7fjEBprn8uh7abyoFK3UgOu4vPtvGgfPMPDiYGzu2snAZ+7jpMbCzzvg+kZOjitGoN6TEv6OHSaLiy4Hv2VurBPr/s89179qdvWsJzWdtYk5KSqrcsNSUNK2srg/GJCYmcCgnl41a9ebdGS27fuMvabUuwsbWmsJMDl85fYd+P+2latx0fNv2U0uVKMnvZlHzLH8DWxobkFP05WympqdhYW79wu1EjB/DzL38Qcvy0wfWVq9SlZOnqqNUavtuyymj5GoVGY7zbv9i/fuI2wMSJEw1Ovn748CEZGRkUL15ct8zOzo5q1arliQ0PD2f27Nncvn2bMmXKULp0zqcyjYYSJUqwfv16Vq9eTZ8+fVCr1bRu3ZoRI0bg4+PD4sWLdestLCzo0qULffv2feGk8ZfhMaA51QY0192POXMDE0szvRiVpRmZSXk/LWblNDpMLM31lptYmpGVpP+GpU7LJDUtnlPTttB07xTMCllx7+AF7h28oIuJDb3J5dX7KN3cn4sr9r5RXS0/a0urz9ro7l8LvYZ5rjzNLc1JzZVnbpk5EzfXT1tHcMd38ahdjRIVS5KZnsEv694sx/+vOvTvQIf+HXT3r5y5Yvi1Sc772nj4e/Buu3cZ2DTv/LD8ok5JQ5nrnFBaZd/Pc5ynpKPKVYvK0gx1UhoKExUeXw3lbOcZZNzP7pW6OmYt9a59jUOQJ7G/ntJto0nLRJOWydWxXxN4cTU2VUqTdOH2G9aRN7cn527eOtJQ5X4fsMp7XhtSyLMs7qPas7dSD7RqDakp6VyYvJHAXRM4O3rdS+3jn/Qe1JVeg7vp7p87dRFLS/2GnqWVBck5vSm5De87Xu/+zAkLaNOpGX61fPjrt0N0adFbt+5e5APmTFnMd798jbW1ld5k7zcxauQARo0coLt//PgZrCz1PxhYWVqSmPT8SfvW1lZ07NCSD5p99NyYtLQ07t1LY/SYaRw9shd7+0LExye8eQHG8C8fJjOW/0RP0vM4OjpiZmbGvXtPJ8nGxsYyc+ZMvasIMjMz6d27Ny1atCAkJITvvvuOTz75RG8btVrN0qVLCQkJYdWqVfz0009s376dqKgoHB0dWbNmDcePH2fWrFmsWLGCAwcOvHH+Fxbv1ps0/fD0dewrldCLsa/oSvyViDzbZiSkkHwvDns3V90yC+dCmDvY8uhKBM5+FWnx92yUpk97NlRmpqjTM8lKSadkI18qfhSst0+luYnuyrc38cPS7XxS5UPd7dqZK5SoVFIvxrViCcKv3s2zram5GQv+WEp5r6dd9UqVEqVKSVJ8EoGt6lOllgdrz21k7bmN1G0RSN0Wgaw9tzHPvkReW5dspXXl1rpb2JkwSlXSH84tVbEUt6/czrNtcKtg7J3s+frw12y7sI3Ppn2Gs6sz2y5so2qNqvmSb3JYOGaOdpg6F9Its65UgrTIGNSJqXlird30zx/rSiVICgtHZW2BqYMNSrOnnxu1ak32xNOMLCxKOhNwYglmRex165Vm2T1MWfFvfnXb47AIzB1tMXey0y2zdXMlJTKWrFx1PA6LwC5XHbaVXEkMy/s+kJuVqyMKpRKF6ulbvyZLDVrQqtUv2PLlrVy4Tm9S9dlT56ngVk4vpnylsly7nHe+o7W1FZ9PGkTxEk8nRKtUSkxMTEhLS6dSlQoMHddfbxszc1M0Gg0ZRpziMHPWYr3J1yHHT1OlSiW9GHf3ily8eOW5+2jcuCEPH8Zy4KD+vMiAWn5cOP+33miDubkZ6enpRmvkCeP5TzeSlEolLVu2ZPHixTx48ID09HS+/PJLQkNDsbB4+skmMzOTtLQ0LCwsUCgUREVFMWfOHN26qKgounfvztGjR1EqlRQtWhQABwcHzp8/T48ePQgLC8PMzAxHR0fdOmO7uf0QRQPcKd3MH4VKSelm/hQNcOfGDsOX0t7YeoBqA1tiU9IZE2sLakz+iPtHLpN0J5pHl+5iYmlG9TEdUZqqsHZ1xHf8h1zf8jeaTDUoFNSY1BmXutn/3Jx8K+D+aSOubvjD6HUd3PkXVWt5UKtpHZQqJbWa1qFqLQ8O7vwrT2xmegYR18LpPPoTbB1sMbey4NOpvbh3M4qrp68wtGF/unl0ortnZ7p7dubQroMc2nWQ7p75d/XOf9n+HfvxDPAk8INAlColgR8E4hngyR878h4Hi0YtonXl1rTzaEc7j3YsHbuUh5EPaefRjosnLuZLfqm37hN/7DKVpnZFZW2BRSlnygxtw71Nea/CvL/tAPa1q1KkeQAKlZIizQOwr12V+9sOkJWQTPyxy5Qf3xlTJzuU5qaUH9+ZzLhE4o+HkRb+kMxHSVSc+gkqK3NMC9viNqsHMb+fJi0i5o3rSL51n5hjYVSb+jEm1hZYlXLGbUgr7m7+K09s+PZDOAVUoXjz7PeB4s39cQqowt3tB//xcWKPX0Gdmo7HlC4ozU0xc7Kj6pgORP10It8mb+/a9hM1a1fn/ebvoFKpeL/5O9SsXZ3d237KE5ucnEJAvZp8PmkQNrbWWFlbMn7m50TcjeLk0dMkPHpM50/b8Wn/LqhUKoq5FuXziQP5fsseMjOMPw/0iQ0btxMUFEDbts1QqVS0bduMoKAANmzc8dxt6tSuwaHDeSfOnzt/CStLS2ZMG4OpqSmlSrkye9Z41n69JV/msr42mbgN/McbSQCjRo3Cw8ODdu3aERgYyKNHj1i4cKFejJWVFdOnT2fp0qX4+Pjw8ccfU6dOHZycnLh69SrVqlVjwoQJTJo0CR8fHzp37kynTp1o3LgxjRo1onv37vTt2xdvb28GDRrEmDFj8PLyMnotj2/c469PF1BtQHM6XlqJ5+CW/N1rIYk37wNQtlVtPry6Whd/dsH3RO4PpdH342l7chEqc1MO9FkMZM9V+r3zbOzdStAudBmNdozj3sELnJi0AYDwX05yYtJG/Kd35cNrqwlc1Jez83Zya+dho9cVdSOSuT1n0Oqztqw9t5G2g9ozr89s7t2KAqBuy3p8c+npxNLlIxbz4O595v2+mEUHVmBuZcGMrlNQZ2YZPbf/7yJuRDClxxQ69O/Atgvb6DS4E9N6TSPyVvYVog1aNmBn2M4CzfF8j/koVEoCTizB7+fpxP1xllvzs69MrXfzW4q2qQtAyvUoznedQ+lBrQi8+jVlhrXhwqfzSL15T7eflBv3qPnnXOqErsC6UglCO0zLnrANnP9kNgoTE2qfWkbNP+aQFhHDxT4LDSf1Gk70+BKFiYp3jy8k6KcpPPjzLGHzs5/bD26spUTrOgAkXY8ipNt83Aa2oMmVr6g8tDXHe3xJcs77wItkxCZyuONMbMoV4/3QpTT4bTpJN+9zZkj+zYe5df0O/buOoPfgbhy/tp9+w3owsPtIbt/M7in+oM37nLr1ty7+s4+HoVSp+O34Dxw49xNOzo70+nAQWVlqHtyLpk+nIbzTuD4hV39n+6/fcv7MJaaOnpNv+QNcuXKDNm0/ZdTIAcREX2Lc2MG079CLa9eye8M+/LAV8XFX9bYpV7Y0UVF5X5Pk5BSaNutM1apuREWE8sfvO/h9/0GGDZ+UrzW8Kq1Wa7Tbv5lC+2+v4D/iW9fnj1v/W+w1Mc6X6hW0DafmF3QKRtHc5+363pXXMSIjf69a+l95rPj3T/8cpblW0CkYxfX4qIJOwSiyMvJ+lY0xPe7dyGj7slu5z2j7+l/795+5QgghhDCuf/kwmbFII0kIIYQQ+qSRBEgjSQghhBC5/Nt/TsRY/vMTt4UQQgghXoc0koQQQgihr4C+AiA2NpZ+/frh5+eHv78/06ZNIyvrxVcuX716FS8vL0JC8n7lwpuSRpIQQggh9GmMeHsFgwcPxsrKioMHD7J9+3aOHj3KunXrnhufmprKsGHD9L4g2pikkSSEEEKIAnfnzh2OHz/OiBEjsLS0pGTJkvTr14+NG5//iwmTJ0/mnXfeybecpJEkhBBCCD1ajdZot4yMDJKSkvRuGRl5v+H92rVr2Nvb637VAqB8+fJERUXx+HHeH23+4YcfuHPnDv3798+zzljk6jYhhBBC6DPi1W0rV65kyZIlesv69+/PgAED9JYlJydjmeuHhJ/cT0lJwc7u6W8b3rhxgwULFrB582ZUKhX5RRpJQgghhMg3vXv3plu3bnrLzMzM8sRZWVmRmqr/g85P7ltbW+uWpaenM2TIEMaMGUPx4sXzIeOnZLhNCCGEEPqMOHHbzMwMGxsbvZuhRlLFihWJj48nJubpj0bfuHEDFxcXbG2f/kTR+fPnuX37NmPHjsXPzw8/Pz8A+vTpw6RJk4z6NEhPkhBCCCH0FMSXSZYpUwZfX1+mT5/OlClTePToEcuWLaNt27Z6cX5+fpw7d05vmZubGytWrMDf39+oOUlPkhBCCCHeCosWLSIrK4uGDRvSvn17AgMD6devHwA+Pj7s3r37f5qP9CQJIYQQQt8rfr+RsTg5ObFo0SKD686cOfPc7a5cuZIv+UgjSQghhBB65LfbskkjSQghhBD6Cqgn6W0jc5KEEEIIIQyQniQhhBBC6NFKTxIgjaS3hoNaXdApvLH3sSnoFIyiuc9nBZ2CUew+s7SgU3hjw/xGF3QKRmGPoqBTeGPrMkoWdApG4epl989BQobbcshwmxBCCCGEAdKTJIQQQgg9MtyWTRpJQgghhNAnjSRAhtuEEEIIIQySniQhhBBC6JHhtmzSSBJCCCGEHmkkZZNGkhBCCCH0SCMpm8xJEkIIIYQwQHqShBBCCKFP++//AlRjkEaSEEIIIfTIcFs2GW4TQgghhDBAepKEEEIIoUerkeE2kEaSEEIIIXKR4bZsMtwmhBBCCGGA9CQJIYQQQo9Wrm4DpJEkhBBCiFxkuC2bDLcJIYQQQhggPUnP2LhxI1OmTGH06NF07dq1oNPJw8zJDq85PXCsXQVtloaIHYe4NHkDWnXeJn+Rht64j/sQq9JFSI2I5dLUjUT/dgYA00LWeEzrSpEGXijMVMSH3uTSpA08vngHAHuf8tTdOwV1aoZufwnnbnGk1RSj1VIi2Au/MR2xKe1McmQsJ77YTMTvoQZjFUoFvmM6UqFtXUwszbh3+BJHRn1NanQ8AGWb+1NvcT/U6Zm6be78cpKDA1fo7cfZtwLvfzeG9eW7G62OF6nRoAbdxnSjWKliREdGs2baGo7vP/6P23084mOCWwXTtXbX/E/yDcQ9iqdz76FMHjWYmtU9CzqdPKrU96b5qM44lirCo6hYdk3fwMU/ThuMtbK3odW4LrgHeWNiZkL4hVv8MG09kZfu6MUplAr6bxpPXMRDNg5fnu81VKzvxXujPsShlDMJUbHsm76Zq3+ceeE2CqWCDssG8SDsLn9+uRMAzxa1aTb9U704lakJaLVMceuaX+lj4liIcnP6YBfggVatJmbHAe5MWQcG3rOeKNykFqXGf0xoQD/dMoW5KaXGdKFw0wBUNhakXY/k7vQNPD5yId9yf5bSwR6HMUOx8PVCq1aT8vPvxC9cYbAO69YfYNupLSonR9QxcSRt2UHS9t0AuP69J9eOFSgtLIgd+wUpv/75vyjlpcnVbdmkkfSMjRs38uGHH/Ltt9/y0UcfYWLydj09visHknbvEb9598Pc2Z4a3w6nXO8m3Fimf+JZl3XBb/UQTvddzIPfTuPStCZ+KwfxR+0hpN1/hNe8nihMTdgfMBh1Sjpun7ejxjfD2O83EMhuJMUevczRNl/kSx12ZYvSYNUg/v5sKeG/n6F0kxo0WDGAHXWHk3L/UZ54r0EtcQ3y4Mcm48l4nErt2d2pM7cHv388FwAnr3Lc2HGYQ0NXPfcxK3aoh/+ULphYmOVLTbkVL1OcsavGMqv/LEJ+D6FO4zqMXj6aHvV6EHs/9rnbedfxpm2ftsQ9iPuf5Pm6Tp+7yNgv5hEeea+gUzHIuYwL3VcM45uBC7m4/zRe79ek29LBTK0/iIQHeY+xTrP7oFQpmf7uMNKTU2nUvzV9vxnDlHoDyUhN18U1HtSW8jXciYt4mO81FC5TlI4rBrNt4BKu7j+D+/s1aL90AAvrDyPRQA0AhYo70mJmDyrU8+RB2F3d8nO7jnBu1xHdfduiDvTePZVfZ2zO1xoqrhhKxv04Tlf/FFNnB9zWjaZYr2bcW74rT6zCRIVLr2aUHNmJzPv6x3+pMV2wrVGZi81HkXH/EUU6BuP27RjOBg0kIzImX2sAcJw+HvXDGKIat0fpVBjneVOx/bAtiRu+04uzDKqD/Wc9eDhoNBkXLmNWrQrOX05HHfuI1D8PEhn0gV584UkjURa2J2X/3/lew6vSags6g7eDDLflOHr0KLGxsYwaNQqNRsO+fft06x49esSQIUPw9fWlYcOGrF+/nipVqhAREQHA3bt36dOnD/7+/jRo0IAFCxaQkZHxvId6LVZliuJUpyqXpm5CnZpByt1ori3YSZnu7+WJLdG+HnEhYdz/5SRatYZ7u48Re+wypbo0BOBUn8Wc6rWQrMcpmFhbYFrIiozYRN329t7liT9706j5P6tCu0AeHL/C3X2n0Ko13P4xhPtHw6jUuYHB+Eqd6nN+6R6So+LITEolZMJ6SjTwxKaUM5DdSIp5Qb515/eiUucGnJm3M1/qMeSddu9w8fhFju47ikat4eCeg5w/dp7GnRo/dxt7J3sGzhrIrrV5/4G8TXb99BsjJ81mYK9PCjqV56rZJoibxy9z/teTaNQazuw9xvWQS9Tu9I7BeK1Wy0/zvyMlPgl1ppo/vtqDnbM9RcoV08VUDKiKV2N/zv4c8j+pwadNPe4cv0LYr6fQqDVc3BvCnZAw/DoFG4x3LOtCnz3TCD9znbsnr75w320W9OXqH6Gc++FwfqQOgHkZFwrVqcbdL75Fk5pB+t0HRH65DZduTQzGV948gUK1PYha8n2edUoLMyLmbCYjKhY0GqI3/Y42PRPrauXzLf8nTEoUx8LPm/hFq9Cmp6OOvEfCmg3YtG+ZJ1bl7Mjjb7aQceEyABnnL5F2KhRzAz2tVh80wsLfl7jxM17Ys1ZQtBqF0W7/Zm9XV0kBWr9+Pe3bt8fCwoJOnTqxdu1amjZtCsDw4cNRKBTs378fjUbD8OHDUavVAKSkpNC1a1eaNm3KwoULiYuLY+DAgWg0GoYNG2a0/GzdSpARl0j6M58gE69EYlXCGRM7K7Iep+jFPg4L19s+8WokhaqUAkCbpUabpabyqPZUGNiCrKQ0jn80Wxdr712O9OgEGhyZj6mtFTFHLnFp0gbS7hmnd8O+Ugke5cov/lokhXPye5aprSXWxR314tNiHpOekExh91IkhcfgWK0MWSnpVOv3AQqVkoj9oZycvoWMhOzn5PSc7aTci8MlwN0o+b+M0pVKcyvslt6yu9fuUrZKWYPxCoWCzxd9zrbl28jMyCSwaeD/Is3XUsffl6bvBWNiomLExJkFnY5BLpVKEHVF/xi7fy0SV/fSBuPX9J6nd9+7sT/pyWk8uBEFgI2jHZ1m9+GrXnNp8Knhf/LG5lzJlQe5aoi+FomLe97zBCAxOp4vg4aQnphKGf/Kz92vV6u6FKlYgk095xs139ys3EqSGZdI5jPvWSlXwzEv4YzKzgr1M+9ZADcGLiLjXizO7fN+WLo1Un/o3K6OByo7K1Iu3soTa2wm5cqgjn+MJuZpD3DWzTuYFCuKwsYabVKybvmTYbUnlA72mPt4Er9Af2hWYW2N/aA+PJq9EE3C4/wtQLwR6UkCIiMjOXjwIJ07dwagffv2XL9+nePHj/PgwQMOHTrEmDFjsLe3p3DhwowZM0a37V9//UVGRgZDhw7F3NycYsWKMWjQIDZu3GjUHE1sLFGnpOstU+cMA5hYW+SKtUCdkqYfm5KOKlfc1S+/56cyn3B13g78N4/CqlQRUCpIu/+I6L/OcrDRWP4KGgFaLTU3fA5K43wiMLWxICtXLVmpGZjmyi871hKAzDy1Z2BibYGFoy2xF+5we+9xdgZ9zt7mk7Er50K9xU/nM6QYqXH3KiytLUnPlXN6ajqWVpYG4zsO7EhyYjI/b/z5f5HeG3FyLIyJiaqg03ghC2tLMnKdAxmp6Zhb5T3GcvN4x5c2k7vx3fg1ZKZloFAo+HhBf/5cvZeoy3f+cXtjMbe2JDNXDZmp6Zg9p4aM5DTSE1NfuE+FQkHQgJb8vXQXGclpL4x9UyprSzSp+o+hyXnPUlnnPQ8y7j1/GPpZNtUrUXHlCCLmfUd6ePSbJ/oPlNZWaNP0n1dNWnZdyueczwBKRwecF84g4/JVUvbt11tn27EV6nv3Sf3tL6PnayzSk5RNepKATZs2kZWVRYsWLXTLsrKyWLt2LX369AGgRIkSunUlS5bU/R0ZGUlcXBw1atTQLdNqtWRmZhIbG4ujo6NRclSnpKGyNNdb9uR+VlJqrtj0vLFW5mQl5XrDSsue6Hxz5U+U6twAl/d9ubnqZ461n64Xd2HsOhpdXIVtJVcSwyJeOXfPAc3xHNBcd//hmRuYWOrPDTKxNCMzKe+bdlbOPwmTPLWbkZWUSlrMY35+Zu5UclQsJ7/Ywgd7JmFibUFWPv8jeKJD/w506N9Bd//KmSuY58rZ3NKc1OS8/8Q8/D14t927DGw6MN/z/K96t19L3vusle7+7dBrmOZ6/s0szUkz8Pw/673+rXm3bws2fb6CM3uO6vadmZ7JgW9+MX7iz6jXrzmBnz19D4oMvZ6nBlNLc9L/oYYXKRtQBdsi9pze+tdr7+NlqVPSUebK/8l9ddLr1eDc6R3KTO5G+Jwt3F/14xvn+DK0qakoLPQbpsqc+5rnvBZmHu44zpxA+pnzxE2Zk2c4zbpFExJWrsuXfI1F5iRl+3/fSEpPT2f79u1MmzaN2rVr65ZfvXqVXr160bt3byC7MVS2bFnd30+4uLhQqlQpfvnl6RtoUlISsbGxFC5c2Gh5JoZFYOZoi5lTITJiEgCwdXMlNTKWrFyfHhPDwinkqT+sY1vJlfjQ7Hk7dX6czM2Ve7m35+mVVkozUzLik7EoXphyvZpwZfY2Xc+V0swUQO9qt1dxbvFuzi1+2g1dfWQ7HD3K6MXYV3Ql5lzervOMhBSS78Xh4OZK/JXsBpqlcyEsHGx5dCUCB/eSlGtZm1Mztj5TiwlajRZNZtZr5fs6ti7ZytYlT3P45PNPKO+hP1+iVMVSXDt3Lc+2wa2CsXey5+vDXwNgYmaCmbkZ2y5sY1K3SVw8cTF/k/8P+G3ZD/y27Afd/abDO1DSQ/8ccKnoyt1zhueumVqY0XXxIIq5lWRh+4lEXLytW1ejdSCFijgw89xaAMwssv/RV3uvBqM8jXel5IFluzmw7Ol50nB4O4rnqqFIRVcin1PDy6jSuAaX950kMzX9n4PfUOqVu5gWtsPUqRCZOe9ZVpVKkh4Vgzox5R+2zkWppOyMXhRu4s+V7rN4fPBcPmRsWOaN26jsC6Es7IAmLnvo0KRcabIeRKNNTs4Tb93sfexH9Ofxym9I3Lgtz3qzKm4oHexJ/f3tm6wt8vp/P9z2448/olAoaNasGS4uLrpbvXr1qFSpEjt37qRBgwbMmTOHhIQEEhISmD376fydBg0akJyczOrVq8nIyODx48eMHDmSIUOGoFAYr5sx+dZ9Yo+F4TH1Y1TWFliWcqbikNbc3Zz3stGI7YdwDKhCsea1UKiUFGteC8eAKkRsPwhA/OnruI1oh2UJJ5RmJlQa0RalmQkP9p0iIy4R11a1qTy6A0pzU8wK21JtRjceHjhPyh3jdG3f2H4IlwB3yjTzR6FSUqaZPy4B7tzYcchg/LWtB/Aa2BKbks6YWFtQc/JH3DtymcQ70aTHJ+Pe7V08+jZFoVJiXdyRGuM/5Pp3B9Fk/O8aSbnt37EfzwBPAj8IRKlSEvhBIJ4Bnvyx4488sYtGLaJ15da082hHO492LB27lIeRD2nn0U4aSK/pxPcHqVCrKj5Na6FUKfFpWosKtapy4vuDBuO7Lh6EQ3FH5jYbrddAApjWcCifV+vGKM/ujPLszqndhzi1+5BRG0iGnP3+EGVquVO1qT9KlZKqTf0pU8uds98bPk9eRik/N24fDzNils+Xdusej0MuUXpKd5TWFpiXLILr4HY83Lz/nzfOpfTkbtgH+3D+/c//pw0kgKzwSNLPnMd+aD8UVpaoirtQ6NOPSN6Vd2jcskEgDqMGEfv5JIMNJAAz72pkhl1Fm57/DdU3IcNt2f7fN5I2bdpEs2bNMDU1zbOuQ4cO7Nq1i2nTpqFQKKhfvz6tWrWiSpUqAJiammJjY8O6desICQmhXr16vPPOOyiVSpYvN/53qJzs8SUKEyXvHF9E4E9TefjnWa7Oz75iq/GNr3FtXQeApOtRnOg2j4oDW/D+ldVUGtqakz0WkHzzPgCXp20m+o9Q6u6ZwrtnlmHvWZajbb8gMyEZTVomxzrOxLZSCd49u4wGR+aTlZjKqV4LjVZHwo177P90AZ4DmtP50kq8B7fkz14LeZyTX7lWtfno6mpdfOiC7wnfH0qT78fT4eQiVOam/NVnMZA93+i3j+dS+n1fOl1cSbOfpxATepNj474xWr6vI+JGBFN6TKFD/w5su7CNToM7Ma3XNCJvZfdCNmjZgJ1h/7ur7f6/ib4Rxepec3n3s1bMPLuWRgPbsrbPPB7eyv7KAr8WdZlzMfsYKVG1LNXe9aNoBVcmH1nGnIvf6G7lajx/AnR+i7lxj8295lPvsxaMPruK+gNbsaXPl8Teyj5PPFvUZuzFNa+0T4dSRZ779QH54VrPOShUKnxCVuCxdxbxf50hYkF246HGtY04tqr3j/swKWyLS9f3MXW2x+uvL6lxbaPu9jLbG0PMqMkoVCqK7dpI0a+XkHr0BI/XbACyv/vI6v3sK4ften4MKhWOsybh+vce3c1h1OCn9bgWQ/0w/7+24E1ptQqj3f7NFFqtjDz+k8OHD+Pr64tFzjj0lStXaNmyJaGhoZibm//D1i/nR5cPjbKfghSjersn876s75QvN4H0bbf7zNKCTuGNDfMbXdApGIX9f2BmQ5OM158L9TZxdU0o6BSMouSJV++RexU3PBoZbV/lL+z756C31P/7nqSXMWvWLJYvX05WVhZJSUksX76c2rVrG62BJIQQQrxNtBrj3f7NpJH0EubNm0doaCi1atUiODgYlUqlNy9JCCGE+C/RaBVGu72K2NhY+vXrh5+fH/7+/kybNo2sLMPzSzdv3kyjRo3w8fGhUaNGRv/qHZCr215KxYoV+eabgp3jIoQQQvzXDR48mKJFi3Lw4EFiYmLo27cv69ato0ePHnpxv//+O/Pnz+err77Cy8uL0NBQevXqhZOTE40aGW+oUHqShBBCCKGnICZu37lzh+PHjzNixAgsLS0pWbIk/fr1M9hD9ODBA3r27Im3tzcKhQIfHx/8/f05ceKEMZ8G6UkSQgghhD5jXrqfkZGR5/dMzczMMDPT/1Lha9euYW9vT9GiRXXLypcvT1RUFI8fP8bOzk63/MkvZDwRGxvLiRMnGD3auBd7SE+SEEIIIfRotca7rVy5El9fX73bypUr8zxmcnIylpb6P/Xy5H5KyvO/gPThw4f07NkTDw8PPvjgA6M+D9KTJIQQQoh807t3b7p166a3LHcvEoCVlRWpqfpfNfHkvrW1tcF9h4aGMmjQIPz8/JgxYwYmJsZt1kgjSQghhBB6jDncZmhozZCKFSsSHx9PTEwMTk5OANy4cQMXFxdsbW3zxG/fvp0vvviCgQMH0r17/nwDvgy3CSGEEEJPQXwFQJkyZfD19WX69OkkJSURHh7OsmXLaNu2bZ7Yffv2MWnSJBYvXpxvDSSQRpIQQggh3hKLFi0iKyuLhg0b0r59ewIDA+nXrx8APj4+7N6d/SPQS5YsQa1WM3DgQHx8fHS3CRMmGDUfGW4TQgghhJ6C+s01JycnFi1aZHDdmTNndH//+OOP/5N8pJEkhBBCCD3yq67ZZLhNCCGEEMIA6UkSQgghhJ5X/c21/yppJAkhhBBCT0HNSXrbyHCbEEIIIYQB0pMkhBBCCD0ycTubNJKEEEIIoUfmJGWTRtJbItpEVdApvDHT/8gnjxEZeb/+/t9omJ9xfw27IMw7OaOgUzCKlGE9CzqFN3brL8O/nfVvsz+qWEGnYBRd83n/Micpm8xJEkIIIYQwQHqShBBCCKFHhtuySSNJCCGEEHr+I7Mn3pgMtwkhhBBCGCA9SUIIIYTQI8Nt2aSRJIQQQgg9cnVbNhluE0IIIYQwQHqShBBCCKFHU9AJvCWkkSSEEEIIPVpkuA1kuE0IIYQQwiDpSRJCCCGEHo18URIgjSQhhBBC5KKR4TZAGklCCCGEyEXmJGWTOUlCCCGEEAZIT5IQQggh9MhXAGSTRpIQQggh9MhwWzYZbhNCCCGEMOA/2ZOUkJDAggUL+PPPP0lISMDGxoY6deowZMgQXFxcaNq0Kb1796Z58+Z06dKFmjVrMmDAAIP7ysjIYPHixfz888/ExsZibm5OjRo1GDx4MOXLl/8fVwYlgr2oMaYjtqWcSY6M5fgXmwnfH2owVqFU4DemIxXb1EVlaca9w5c4PPprUqPjASjbzJ/6i/uhTs/UbXPnl5P8PWgFAOWa18JnSCusXAqT+jCeC6t+JmzDH0apwzXYi+pjOmJTOruOU19sJvL359dRfUxHyrWti4mlGfcPX+LYqKd1uNSpgs+o9hSq6Io6NZ07e45zatpm1GnZdfnP6EqFDkFostS6fZ6cvJFrG/987fxNneyoPLc39rWroM1S82DHQa5PWo9WnbeT2rGhD+XHd8aydBHSImK4PmUDsb+dBkBpYUrFKV1xalwDpZkJiedvcW3CNyRfuguATdXSVJzyCbae5dBkZhH3RyhXx68j61HSa+f+T6rU96b5qM44lirCo6hYdk3fwMU/ThuMtbK3odW4LrgHeWNiZkL4hVv8MG09kZfu6MUplAr6bxpPXMRDNg5fnm+5v464R/F07j2UyaMGU7O6Z0Gno6Owtcey21BMKnuh1ajJPPI7aVtWgOb5AyFK1zLYTFxK8vwxqMPO5llv0fkzFJbWpK6enZ+p65g4FqLkjM+wqeWBVq3h0fd/ETltLRg4T54o1DiA4mO6cTmwl95yx48aU6RnS0yc7ckIf8C9Wd/y+I+T+V2CjmuwF37PvGed/GIzES94z/Id05HyOe9Z9w5f4ugz71nPxjXaOoakiIccGrIq/4t4BTLclu0/2ZM0ZMgQHj16xPbt2wkNDeWHH34gIyODbt26kZWVxd69e2nevPlL7Wvq1KmcOXOGdevWcebMGX799VdcXFzo3Lkzjx8/zudK9NmVLUrDVYM4PWc76917cXreToJXDMDKxcFgvPeglrjW82BXk/Fs8RuIOi2DwDk9dOudvcpxfcdhvnXrobs9aSA5uJWg7tweHBi2ivXuPTkwZBW1JnehaE23N67DtmxRglYNInTOdrZU7sXZeTsJWjEAy+fUUW1QS4oFebC3yXi2+w4kKy2DgLnZdZgXtiX4m+FcXb+fLe692NNoHEUD3PH4rJlue0evchwduZbNlXrobm/SQALwWDUYdXIah716c7LxGBzqVaNk76Z54izLuuCxZhg3Z23lQIWu3JqzDY9VQzDLqbXs8PZYlS9GSOAQDnn0JOniHTy/Hg6AwlSF16bRPDp0kYOVu3Os1kDMitpTcfInb5T7iziXcaH7imHsnb+VkdW68fOC7+i2dDCFihp+bTrN7oO1gy3T3x3GWL9e3Dp5hb7fjMHM0lwvrvGgtpSv4Z5veb+u0+cu0rn3UMIj7xV0KnlYfTYebXoqjwe3J2nyZ5hUrY5Zo7bP38DMHKu+Y1GYW+RZpbC2w7L3aMzfa52PGedVeukI1ClpXKjZlavNh2FT1wvnHi0MB5uoKNK7NWUWj0Ch0B/qcWgTjMugjtwZOJfzVTrwYOl2yqwYjUmRwv+DKrLfsxqsGsSZOdvZVLkXofN2Uv8F772eg1pSPMiDH5uM5zvf7Pfe2nN75InzGtqaIv5v/p6aHzRGvP2b/ScbSadOneLdd9/F2dkZACcnJ8aMGYOXlxePHz8mODiYnTt36uLv3r1Lly5dqFGjBh07duTcuXN6+woMDKREiRIA2NnZ8fnnn9OgQQMePnwIQJcuXZg5cyatW7fG29ub1q1bc/Kk8T/hVGwbyIOQK9zZdwqtWsOtPSHcOxaGW+cGBuMrfVifc8v2kHwvjsykVI5OXE+JBp7Ylsp5XrzLEXPupsFt7cq5oFSpUChyDhGtFq1Go9fr9LrKtwsk+vgVwnPquPNjCA+OhlHpOXVU7FSfi0v3kBKVXceJCetxbeCJTSln0uMS+c6rHze+OwhaLeYONqgsTEmLTQRAaWaCQ+WSxJ41XOfrsCxTFIc6HlyfsgFNagZpd6K5PX8HJbq/nye2WIcgEkIuE/PzCbRqDdG7jxJ/9BKuXd4BwKqSKygUuptWrUGdmgGANlPN0YBB3P5yJ1q1BhN7a1RWFmTG5l/jvGabIG4ev8z5X0+iUWs4s/cY10MuUbvTOwbjtVotP83/jpT4JNSZav74ag92zvYUKVdMF1MxoCpejf05+3NIvuX9Onb99BsjJ81mYK/8a3S+LmWR4pi4e5O2dRVkpKN9eI/0XRswf6flc7ex/HgQmacO511hboHNrHVoU5LIPHEg/5LOxax0MWwDPImavg5tWgYZ4Q94sGgrzh/n/TABUH7DZGxqV+PB8h151hXp1ZJ78zaScvYaAPG7D3Ct9Qg0SSn5WsMTFdoF8uD4Fe7mvGfd/jGE+y94z6rUqT7nn3nPCpmQ/d5rk/PeC9k94KWb1ODOTyf+JzWI1/OfHG5r2rQpEydO5OTJk9SsWRMvLy9cXV2ZOXOmwfj9+/ezcuVKvL29Wb16NT179uS3337Dzs6Opk2bsmTJEm7dukWtWrXw8vKibNmyzJgxQ28fW7duZfny5VSvXp01a9bQt29ffv31VxwcDH/SeB32biWICwvXWxZ/NRJH91J5Yk1tLbEp7sijZ+LTYh6TnpBMYfdSJIbH4OhRhqyUdDz7foBCpST8j1BOTN9CRkIKkX+dJ/rMdZrtmogmS43SREXIlI3EGKGxYV+phF5eAPHXInGoYrgOawN1ZCQk4+BeiqS7D8lKTgOgzclFWBcrzINjYdzYmv3PwKFKKZSmKryHt6FITTcyE1O4tvlvLi7fC9rX+0pZ68olyYxLJOPBI92y5KsRWJR0xsTOiqzHT9+4rd1KknT5rt72yVcjsKlaBoDw5T/isWYY9cLWoslSkxmXyJnWk3WxmpR0AKr/OAX7mpVJCgvnzrLdr5X3y3CpVIKoK/qvzf1rkbi6lzYYv6b3PL373o39SU9O48GNKABsHO3oNLsPX/WaS4NPm+RP0q+pjr8vTd8LxsRExYiJht8bCorStQyapMdo42N1y9RRd1A6FQUra0hJ1os3rfMuyqLFSV0zF4uWXfR3lplB0phP0T5+hOX/tXff8U3UbwDHPxlt00EptIVC2aPsvfcSBJkiIILsDYIowo+NUIYMQYbIVlmK7KkgspEle5UNLW1paQt0pk2T/P4IBELCKLSG1uft614vc/e96/Mld5cn33HpOezfCB8AjV8ekh9EkxweZV6nvRaEY65sqNxd0Udb1iFw8Cx09yLJ2qa+xXqFxhGNXx4wGCj02xQ0frlJvBlMyJSfMcRr/5W6vOielTUF96wn997YwPtoPN2pMaMne3p8R4le1l+u3gUycNskQ7YkTZw4kbFjxxIaGsrYsWOpX78+DRs2ZMsW2x8ubdq0oVKlSjg4ONC3b1+cnJzYv38/AAMGDGD27NnEx8czdepUGjduTK1atfjpp58sjvHRRx9RtWpVHB0d6du3L87Ozuzd+3ZdOs9zcNWQnJBosS45IQm1q3XzuoObs2l7vO3yGs9MRF24w63tx1lXdxhbW47HPb8Pdef0B0DppCYm8D6/t5/CT4W6s6vzDMoP+Qjf2iXfvh5uGqu49AlJOLxBPZ61qeYQ1pb/DKPBQJ1FgwBwzOTCvSOXCVi2i3UVB3Fw4A8U696IEn3e/ANb5eqM/rl4DPFJj7dZxqRy01iV1SckoXI1dUcp1Crubz/GoTJ9OOjXjYjfT1D656EonRws9jnT1p8Dft2ICwik3NoxoEybG5jG1Zmk5z54khIScXKxfm+eV/K9Cnw0vhu/jVmKTpuEQqGg86zP2LtkOyGX77xy/3+bl2dW1GqVvcOwSeHsAokJliuTTO+LwsnZYrUyR240H3Un/ofJYLTRuWEwYIx+YL0+jancnM1JvjmUx/cvpY3zSXcv0modgDqzGwqlkmy9W3F31HwuVurKg80HKPDzOBxzZUv9wG140T0rJfdec3mFglpz+3Fx0e88uBRotf+7wqBIvSU9y5AtSUqlkpYtW9KyZUuMRiM3btxg8+bNDBs2zNwF96wnXWkACoUCHx8fwsLCzOvq169P/fqmbzeBgYHs2rWLGTNm4OrqStu2bQHIly+f1TGedMe9qTKftaDMwKdjp+6fvoFa42hRRu3siC7O+ttU8uMPOvVzY0PUzo7oYhPQRkSzvc1E8/q4kEhOTPqVFlu/xsFVQ/khH6FP1BFy6CIAQXvOcHPzEYp8Wp/gAxdSVI+SA1tQ6pl6RJy+gdrZsh4qZ0d0sSmrR3Ks5YeIXqsjQfuQk5N+pen2CThmdiH04AVCDz6NN/LMTS4v2UneFlW4uGB7iuph/jvxWpTPxa90Mb22iik+EdVzsaucHdHHalGoVZRc/CVnO04h6Z7pQ+zqyGXUvvYjWeqUJnLXSfM+Bq0Og1bH1VE/UuviEtyK5yX2wu03iv9ZDfu3otGAD82vb5+5hsNz8To6O6GNS3h+VwuNPmtNw34tWT1sAae3HTEfW5eo48DPf7x1nP81xsQEcHzuA/jxa6P2mffCwQGX/mNIWD0fY1T4vxjhqxnitSifO5eevNa/4nyyOE6SqYs/fMlmtNdMrTMRP2/H89MmZKpXgcgVv6dSxE+VGtiC0s/fe9/ynqV6fO8tPbA5+kQdAT/+mepxi9SX4ZKkgwcPMmjQIPbu3YuHhwcKhYJChQoxZMgQDh8+zKVLl6z2CQ9/enMxGAyEhITg6+vLjRs3aNWqFevXr8fPzw+APHny0LNnT86ePcvly5fN+z2bVD05Ro4cT8dlvImz87Zwdt7T1q8Kw9riWSqfRRkPP18izt6y2jfpUTxxoVF4+Pny4MpdAJy9M6PJkokHV+6SpVhuCraqzj9T1pj3UTmqMRqM6HXJuOX0JPGhZXO4QafHkKQnpS7M3cKFuU/rUfZ/bfEs+Vw9CvsSee4l9Sjiy8PH9dB4Z8bpcT28Kxam+re92PreCAw6/eN6OKBP1JEcn0ju9yug8c7MtWdm5Smd1OaZb28iLiAIR093HLwzo7v/CABXv1xogyPQxyRYlc1UKr/FOle/XESfvYnKVYNDFjeUjk8vQ6PegNFgxJiUjCa3N+U2jONk09EkPZ4Vo3Q0tTAlP0yd2W1/zt/En/M3mV83/epjcpe0jNensC+BLxi75qBxpOvcz8lRJDez243j7sXb5m2VWtcic7YsfHNuGQCOGtOHRqlGlRheunuqxJ9RGe7eRpkpMwr3LOZWIFXOvBgiwyHh6XWpyl8UpU8uXLp/Bd2/Mq93/WISSYd3oV0+51+P/YmEK3dQZ3VH7eVBcsRDADSFc5MUch9DzOuPJdI/iEF3/6H53H9CoVRaDfBOLefnbuH8M/escm95z3py73145S4VR7XHOXsWOlxaCGD+EpXn/QqsLt4nTerzJuS320wyXHdbpUqV8PT0ZMSIEVy5cgWdTkdsbCxbtmzh9u3b1K1b12qfdevWcfbsWfN0f7VaTZ06dShQoAAlSpRg7NixnDt3jsTERBISEti/fz/Hjh2jYcOG5mOsXbuWCxcukJSUxPfff4/RaKRePduD+t7U9fWHyFGtGPmbVUGhUpK/WRVyVCvG9fWHbJa/uuYAZT9vhVtubxxcNVT9+lNCj1wm5k44iQ/jKN61IaX6NUWhUuKa05PKoz/h2tqDGJKSCfzzFAVaVMG3TikAfKoWpWDrGtzYaGNgaArdXHeI7NWKkbe5qR55m1che7Vi3HhBPW6sOUCpQaZ6qF01VBr/Kff+vkzsnXAeXApE7exI+ZHtUTqocPX1pMKYT7j+635T0qRQUOnrjvjULAGAV4VCFOvxPlff4lEGCbfu8fDoZfz8u6Jy1aDJ402+Lz8idLV19+q9tQfwqF6CbC2qoVApydaiGh7VS3Bv7QGSH8Xx8OhlCo7piIOXO0onBwqO6YguKoaHxwPQBt1H9yCWwv5dULk44ZA1E0Wm9iRi9ym0dyPeOP6XObHxIIWqlqBc06ooVUrKNa1KoaolOLHxoM3yXed+TpacnsxoPsIiQQKY1OBLhpXqxvDS3Rleujsntxzi5JZDkiC9BkNYMMlXzqPp2B80zii8fHBq+SlJByxbTfRXzxPd6wOi+7c0LwBxs0bZNUECSLodSuzxi/iO7YnS1RnH3NnJPuhjotbsTvGxIlf9TvZB7XEunh9USry6NsPBJyuPdh5Ng8it3Vx3CJ9qxcj3+J6Vr3kVfF5yz7q+5gBlnrlnVX58z4q5E87GOsNYXbQXq4v3YXXxPtza9De3Nv39TiVIAMZUXNKzDNeSpNFoWL16NfPmzaNfv35ERkbi4OBA2bJl+fHHH20+26hRo0aMGzeOwMBASpYsydKlS3FxcQFg8eLFzJ8/n6FDhxIWFoZSqaRYsWJMnz6datWqmY9RuXJlJkyYwPXr1ylevDjLli0jU6ZMqVq3RzdC2d1jFpVGtqfmjJ7E3Y3gr96zib51D4CCH1anxjfdWV7ENNX09HcbUTqoaLZhDA6uGkL/vsSevnMBiA+NYleXGVQc3o6yg1qhT0zi5pajnJj0KwBXf92P2tmRqhM645LNg7jgCP4e+eMLn8mUEtE3QtnXYxblR7Wn+oyexN6NYH/v2cTcNNUj/4fVqTq1O7/4mepxdtZGlGoV72801ePe35c48LgeyfGJ7O44jUrjO9H2zHx0MfHc3HCYc99tAiDoj3848fUqqkzuikuOLGjDH3H22w3c2vB2yd75njMpMrk71U7MA6ORe78d4NbMdQDUvrmcK0MXEbb+EPHXQzjfdToFx3Sk6Ky+aO/e50KPb0m4GWo+TqGxn1J57wyUahWPTl7jzMeTzGM5zneZRuGJ3ah+cj6GRB33fz/Bjcmr3yr2lwm/EcKS3jNoMbwDn0ztS1RwBMv6fsv9W6Z4K7asyceTezG0RBdylchPqYYV0SUmMf7v+RbH+aHrFG6eCEizOP8L4ueNx7nTQDLNWAVGA7rDf5K4eSUA7gu3kfDTLHRH/rJzlC93u99Uck3oQ/FDizEaDDzYsJd7c0yt16UureHuyPk82LT/lce5992v6GMTyDtvKA4+niRev8vNrhPQhUW9ct/U8OhGKHt6zKLiqPbUeHzP2tt7NtGP71kFPqxOtandWfX4nnXm8T2ryTP3rH2P71nphb2m7kdGRjJmzBiOHz+OSqWiRYsW/O9//0Ottk5X9u/fz4wZMwgKCiJHjhzmmeepSWE0vuEUH2H2qgdSvo6luT5NxYjswyGDnEm5kpPsHUKq2OSc/pvLv/1nyqsLpQPxQ3rZO4S3dmufq71DSBVnkt3tHUKq6Bq8Mk2Pv8GnQ6odq/W91/9i16lTJ7Jnz46/vz8RERH069ePVq1a0bOn5XOmbt++TYsWLZg5cyZ169Zl165djBgxgl27dpE9e/ZUiz3DdbcJIYQQ4u0YFIpUW17XnTt3OH78OEOHDsXZ2ZncuXPTv39/Vq1aZVV248aNVKxYkffeew+1Ws0HH3xApUqVWLNmjY0jv7kM190mhBBCiLeTmh0DSUlJJCVZttA7Ojri6Gg5Y/DatWt4eHhYtAQVLFiQkJAQoqOjcXd/2gp4/fp184SqJwoVKkRAQOp280uSlApWrFhh7xCEEEKId9LChQuZN2+exbrPPvvMaohKXFwczs6WzwF78jo+Pt4iSbJVVqPREB+fuk9hlyRJCCGEEBZSc+B2nz596Natm8W651uRAFxcXEhIsHyMypPXrq6WY+KcnZ3Rai2fU6XVaq3KvS1JkoQQQghhITWflG2ra82WwoUL8/DhQyIiIvDy8gLgxo0b+Pj4WM0W9/Pz4+LFixbrrl+/TsmSb/+rEM+SgdtCCCGEsLt8+fJRoUIFJk+eTGxsLEFBQcyfP582bdpYlW3RogXHjx9nx44dJCcns2PHDo4fP07Lli1TNSZJkoQQQghhwYAi1ZaUmDNnDsnJyTRo0IB27dpRq1Yt+vc3/aZouXLlzL/BWrBgQb7//nsWLlxIpUqVmD9/PnPnziV//vwvO3yKSXebEEIIISzY67F3Xl5ezJlj+2nxp0+ftnhdq1YtatWqlabxSEuSEEIIIYQN0pIkhBBCCAupOXA7PZMkSQghhBAW7PXbbe8aSZKEEEIIYSGD/BTnW5MxSUIIIYQQNkhLkhBCCCEsyJgkE0mShBBCCGFBxiSZSHebEEIIIYQN0pIkhBBCCAvSkmQiSZIQQgghLBhlTBIg3W1CCCGEEDZJS9I7wl2f/p9K4WnQ2TuEVBGtyBiXhUcKf1jyXRQ/pJe9Q0gVLt8utncIb82lVn97h5AqvKP09g4hXZDuNpOM8WkghBBCiFQjSZKJdLcJIYQQQtggLUlCCCGEsJD+B4CkDkmShBBCCGFBnrhtIkmSEEIIISzImCQTGZMkhBBCCGGDtCQJIYQQwoK0JJlIkiSEEEIICzJw20S624QQQgghbJCWJCGEEEJYkNltJpIkCSGEEMKCjEkyke42IYQQQggbpCVJCCGEEBZk4LaJJElCCCGEsGCQNAmQ7jYhhBBCCJukJUkIIYQQFmTgtokkSUIIIYSwIJ1tJhkySapfvz73799HrTZVz2g04ubmRvPmzRk6dChKZer1Ms6dO5fjx4+zYsWKVDvms3zql6H06E9wzetNfHAk5yb8Quju07YLKxWUHtWevG1roXJ2JPzQJU79bxna8IcAOHm6U2FGD7yrFcOoN3Bn/SHOjV+NUW/5nSFrhULUXTeKDfm7mdc12jcV11xeFuXUrhrOT15DwNwtr10fBy93is7og0f14hiT9YStP8j1r1dYxQDg2aAcBcd0xDlvNrR3I7g+YSWRf54yVVXjQOEJXfFqUgmlo5qY87e4NvZn4i4FAqDJ7U2h8Z3xqFIMFPDo+BWujf0JbeD91471ZRy93Ck3vSde1YthSDZwd/0hLoxfZbMe2RuUpfjo9rjmzUbC3Ugu+K8m7E/r97CUf2cc3J059flC8zqXPNkoPaUrWcsXwqjXE7b3HOdH/YwuOj5V6mFL4bplaDT8E7Lk8eZRSCQ7J//C1T0vOOceUygVfDz/c8ICAtn73QYASresTvPJPSzKqRzUYDQyoUjXtAofRSYPnLt9ibpoGYwGPbq/d6P9dQEYXvzdWOmbD7dx3xM3cyT6gLNW2zUdB6BwdiVhybQ0i/ttRD14SMc+XzJ++GAqly9t73DMVFkzk3385zhXLg16PdFb93B/2mKwcZ1k/vgDsnRpjTpbVpLvR/Fg+SYe/bINAIWjA15DuuPWqCZKVxeSbgYRMXMZCcfPpUncjl7ulJrRE8/qxTEmGwhef4jLX6+0eX17NyhL0TGf4JI3G9q7kVyesIrwx9e3OrMrJSd3xbteGRSOKh6ducnlcSuJvniHLFWKUPmX4RbHUqhVqJwc2F26P4lhD9Kkbq9LWpJMMuyYpPHjx3P69GlOnz7NmTNnWLp0KZs2bWLevHn2Du21ueXPTvUlg7kwbS2b/Hpxcfp6qi4aiMYni83yxQd/SPY6pdjdeDTbyg1Er02i4rc9zdurLhxIcpyWbeU+468mY8heqySFezexOEa+9nWo/etwVBpHi/W76v6PjYV6mJeri37nwfnbXFu6M0V1KrloMPo4LYfL9OGfJiPJUrsUufs0tSrnnN+HkkuHcHPqGg4U6sqt6WspuegLHB/XPf9X7XApmINjtb7gUMlexF68Q+kfvzLvX+qnoSQ/jOXvSgM4UnEAugcxlF7+vxTF+jKVHv9b/lF2APubjMG7dkkK9vnAqpxrfh8qLxlMwNR1bC/ck8sz1lFp4SCL99AhixsV5vWnYK/GVvtXXPAZMVfu8nupfuyu+RUuubwo+XXHVKvH87Lmy077BYP5a+ZaJpfqxZ5Z62n3/UAyZbd9zgFkzulJp5+GUbxxJYv15zb/zaQSPczLnPpfEf8ghk3/W5xm8QO4DBiDMTGB6MHtiB0/AHWJ8ji+3+bFOzg64dJvFAonjdUmhas7zn1G4NSodRpG/HZOnbtIxz5fEhQcau9QrOSYORJDvJabdToS2O5zXKqVI0sX639L1wbV8PqiG/dGzOB6xdbcG/EtXp93wa1hDQC8hnTHuVwJgj75ghtV2xK9/g98f5iAOod3msRdftEg9HGJ7C7Tn0NNRuNVuyT5bVzfLvl9qLD0C65OXcuuQj24On0d5Rd9jtPj67v0zF6oMzmzt+pg/izam4enb1Dh5yEAPDh2hZ0FupmX3aX6EX8rjCvf/Gb3BEk8lWGTpOcVKVKESpUqcenSJcLCwhg8eDD169enTJkyNGjQgHXr1lmUnThxIlWqVKFv374AbN26lWbNmlGuXDmaNGnCjh07zOXj4uIYPXo0NWvWpEqVKsyaNStVYs7brjb3j18h5I+TGPUG7m49xv0jART4tL7N8vk71CXg+20khESRHJvAmTHL8alfBtc83rjmy062GsU55/8L+oQk4gLvc2nWJgp1b2Tev+Ks3hToWI+LM9a/NC7v6sXx692Eo33moI9PfO36OOfLTpYaJbk+YSWGhCS0d8K5PXM9ubpbJwc5Pq7Do2OXifj9BEa9gfAtR3h45BK+nd4DwMXPFxQK82LUG9AnJAGmb29J4Q+5+c0aDPGJ6OMTCVq8A7dieVBndn3teF/ENV92vGuU4KL/avQJScQHhnNl1kYKdG9oVTZPu1pEHgsg9I9/MOoNhGw5RuTRy+TrZHoPVS5OvHfoW3TR8QRvO2a1f6bCOVEoFSiUj+tpeFrPtFDuo9rcOX6FgF0nMegNXNx+jDvHAqjYwfY555nfh77bJhF0+jqB/1x96bE/mtWPq3vOcG7T4bQIHQBltpyoi5VFu2YRJCVivB9K4uaVOL3X6oX7OHf+HN1JGzE5aXCb+hPG+Fh0Jw6kWcxvY/OOP/nf19MY1LuLvUOx4pAnBy5VynB/xhKM2kR0d+8R+cNqPDo2tyqrzuZJ1JLf0J4NAEB75jLxx8/hXLEUAAonJyLmLif5XgQYDDxa+wfGJB2aEoVTPW6XfNnxrFGCyxNWY0hIIuFOONdmbiDvM/fKJ3J9XJuoYwGE/W66vkO3HCXyyGXydGoAwOk+cznVazbJ0fGoXDU4uLuQFBlj8++WmNwV7b0ors/amOp1ehMGReot6VmG7G57nk6n49SpUxw9epSBAwcyevRoPDw82L59O46Ojixfvhx/f3+aNGmCq6vpQzQwMJB9+/ah0+k4duwYI0eOZN68edSqVYtDhw7Rv39//Pz8ALh06RJdunTB39+fY8eO0bVrV+rWrUu5cuXeKu7MRXx5dDnIYl301WA8SuSxKqvO5IyLr6dF+cSIaJIexpG5eB4wQmJUDNqwhxbHcs3lhYO7C7roeC5OW0dCaBTe1Yq9OCilggpTu3Fp1kZib4WlqD6uRXOji4oh6ZlvSXFX76LJ7Y3a3YXkZ7qQXIvkJvZyoMX+cVfv4lYiHwBBP2yl5NIh1A5YhiFZjy4qhtOtxwOQ/CiOs59Mttg3W7OqJASGk/woLkUx25KpSC6Snvu3jLkSjEsub/O/5bNlowMs38OYq8FkLp4XAEOijj11hpIYEU352X2s/lbAjA2UGNWeAj0bo1SriPrnGhcn/vLWdXgRbz9fwq5Yxht+LRifYtbnHEBM+EO+q/MFiTEJ5KtS9IXHLfNhTbIVzsXqXjNTNd7nKX3zYYiNxvgw0rxOH3IHpVd2cHGFeMv336FGQ5TZc5KwdAaaVp0sD6ZLInZkD4zRD3DuOSxN435TNapUoGmj+qjVKoaO+8be4VhwLJQX/cNo9PejzOuSrgfikDM7ykyuGGKevhdPutWeUGXNjEvFkoRPXQRA+NdzLLY7VymDMpML2ss3Uj3uTEVN1/ezrTmxV4NxsXGfylQkFzHP3aNjrwbj/vgebUzWY0zWU2REOwoOaklyrJYTHa27bLNUKULOltXYX3NIqtfnTckjAEwybEvS+PHjqVixIhUrVqRatWr4+/vTrVs3Pv30UyZOnMi4ceNwcHAgJCQEV1dXtFotjx49Mu/frFkznJ2dcXd3Z9OmTTRq1Ig6deqgVCqpXbs2q1evJnv27AAULlyYli1bolAoqFq1Kl5eXgQGBr4otNemdnVGH6+1WKdPSETtYt0t4ODmbNpuVT4JtasGtZsGfULic9sSH/8d0/ESQqN4lTyta6B21aS4mw1A5eps1fJkiE96vM2yTio3jVVZfUISKlcnwNR3f3/7MQ6V6cNBv25E/H6C0j8PRenkYPV3c3ZuSJ5+zQkYstBq25tQu2lIfi625Mf/ls/XQ+3mbFVWH59kLmfUG0iMiH7xHzMYuDJrI9v9erKz4iAAyk7r8eLyb8nJ1Rndc+eQLiERRxvnHEBSnJbEmISXHlOhUFBnYCv2f7+ZpDjtS8u+LYWzCyQ+F0+S6W8qnJwtVitz5EbzUXfif5gMRhsjMAwGjNHvdreHl2dW1GqVvcOwSenqguG5c8moNV0LShdnW7sAoPLKgu+iiWgvXidm216r7ZoyRcn53Sgiv19FcnDKvqi9DrWN+9ST12qr61tj+x79XLlrszbyR94uXJuxnsq/Dsc5bzaL7X5D23Dn5z9JuBuRWtXIkOLj4xkxYgRVqlShQoUKDBs2jLi4F3/x3blzJy1btqR8+fLUr1+fefPmYXjJ2ERbMmxL0rhx42jd2vY4gqCgIKZNm8bt27fJly8fefM+/lb/zD9etmxPT+Lw8HCKFy9ucYzSpZ8OjvTw8LDY5ujoiF6vT3HMRQe1oNiglubXkaeuo3J2siijcnYiOc76Qyn58YVqXd6R5FgtKBU2jwWgi335h9yzCnxaj5sr92LQ6l57nyf08VqUzpZjnZQuptfJz8Wgj0+0WRd9rBaFWkXJxV9ytuMUku6ZPsSujlxG7Ws/kqVOaSJ3nQRA4aCi8ISuZG9VnbOffsPDwxdTHLPteljHpn782roeWlTP1Vnl4mhVzpbMpfNTbHg7tvv1xKg3kBCfyIXxq6i1eSxnR/z0Wsd4ldr9W1BrwNNzLvjMdRyeq5uDsxOJNs6515W/WnEyZfPg1Jp9b3yM12VMTADH5xK6x6+N2mfq4OCAS/8xJKyejzEqPM3j+i8yJmhRPncuKTSm14Y42xMPNGWKkmPWKBJOXiBs5EyrAd7ubRqTbXgfIuau4OHPG9Ik7uR4rfW9x8X29Z1s8z7lZLrnPuPJ/fLWwh3k7lgPn8YVuLXwdwBc8mbDs3pxzn2ROl/iUsu72I7k7+9PaGgoO3fuRK/XM3jwYGbMmMG4ceOsyl64cIFhw4bx3XffUadOHW7dukWvXr1wcXGhe/fur/03M2xL0ovodDr69OlDy5YtOXbsGL/99htdulj35ysUTztSc+TIQUhIiMX2ZcuWcerUqVSNLWDOFovB0VGnruNeJJdFGXc/Xx4F3LXaV/conviQKIvyTt6ZccqaiUcBQUQHBOGUNRNOXu4Wx4oPjiT5FS0B5uN5ueNVyY876w6+Uf3iAoJw9HTHwTuzeZ2rXy60wRHon4shLiAI1+fq7uqXi9iAIFPffhY3lI5Pc3yj3oDRYMSYlAyAQ9ZMlN80nswVC3Pi/eGpliABRAfcxcnT8t8yUxHb/5bRAXet3sNMfr7E2HgPn+fi64lCqUShenqZGpL1YATjGyThthyYv8VicHXQ6etk87OMN1thX8KvvDreFynepBKXd/6DLuH1x6+9KcPd2ygzZUbh/nSguSpnXgyR4ZDw9BunKn9RlD65cOn+Fe7zN+M+fzMArl9MQtN5UJrH+V+QeO02qiyZUXl6mNc5FsqDLvQ+hljrJMm9dSNyLfuGh8s3cm/oVIy6Z76IKZVk+3oQXl90JWTghDRLkABiAu7i6JkJx2fuU25+viTYuL5jA4Jwe+76dvPzJeZxF3v1bePxaVbZYrvSyYGkB0/PRZ9mVYg6foWEoHerFcmQiktqSEhIYOvWrQwaNAgPDw88PT356quv2LBhAwkJ1p9hwcHBtG/fnnr16qFUKilYsCANGzbkxIkTKfq7/8kkSavVotFoUCgUhISEMH36dPM2Wz788EP+/PNPDh06hMFg4ODBg8ydO5dMmTKlaax31h0iW7Vi5GpeBYVKSa7mVchWrRh31h2yWf72mv0UH9zK1HfuqqHshE6E/32JuDvhxN4K4/6xAMpO6ITaVYNLbm+Kf9GKW7/se+14vCr7kXDvIXFvOI0+4dY9Hh69jJ9/V1SuGjR5vMn35UeErrZuUr+39gAe1UuQrUU1FCol2VpUw6N6Ce6tPUDyozgeHr1MwTEdcfByR+nkQMExHdFFxfDweAAKtYoyv44iOTqek83HpNq0/yfibt0j4mgApfw7m/4t83hT5IsPCbTxbxm07hBe1YqTs4XpPczZogpe1YoT+BqJZuTxK+gTEik5oRNKJwccvdwpMfJjQnacSLPB22c3HiJf1WKUaFoFpUpJiaZVyFe1GGc32j7nXkeeikW4fTwgFaN8MUNYMMlXzqPp2B80zii8fHBq+SlJB363KKe/ep7oXh8Q3b+leQGImzUK7fI5tg4tUkh3J4T4fy7gPaIvChdn1L7Z8ezXgej11l31bg1rkG3cZ4QM8ufBT9YJkPfwPrjWqkhg20HEH3n54yjeVvyte0QdDaC4f2dUrhqc83hT+MvWBNm4T91dewjP6sXJ0aIqCpWSHC2q4lm9OMFrTdf3g1PX8RvaFudcXigd1RQe2galo5qwnSfNx8hapQhRR/+d68NekpKSiI2NtViSkqzvYVqtljt37rxw0el05rHAAAULFkSr1XL79m2rY73//vuMGDHC4tj79u2jRIkSKYo9w3a3vYiLiwuTJ09m9uzZTJw4EU9PT9q1a8f169e5evUq+fPnt9qnQoUKTJ06lalTpxIcHIyvry8zZ86kcOHC/PHHH2kWa8z1UA53n0npUZ9QcWYv4u9G8HfP74i9eQ+APK2rU2GaqdUJ4NLMjSjVKuptGouDm4bwvy9xtPdc8/GO9JxNucld+eD4dxgNBu6sPcSlFMykcM2TjYR7rx639DLne86kyOTuVDsxD4xG7v12gFszTTMLa99czpWhiwhbf4j46yGc7zqdgmM6UnRWX7R373Ohx7ck3Aw1H6fQ2E+pvHcGSrWKRyevcebjSRjiE/H+oDLuZQqgT0ii5qWlFn//WK0vSAyOtIorpU70/I7SU7rR8PhsMBoIXHuQgJmmm3uzG8s4M3QpdzccJvZ6CMe6zaTE6PaUm9mbhLsRHO/5HXGP38OXSYqM4XD7bygx+hMan/kevTaJe7tOcdE/7QZuR9wI5ZfeM2k4/BNaTe3Fw+AIfu37HZG3TPE+efbRpBKvPy4qS55sxPyLU5rj543HudNAMs1YBUYDusN/krh5JQDuC7eR8NMsdEf++tfi+S8LHTyRbKMHUGD3TxgNRqI37ybyh9UAFPpnI2FfzyFm2148B3yKQqUi5+zRFvtHb91DxOyf8ejQDPQG8m2x7JJ6sn9qO9nzO0pO7kr9E3MwGg0E/3aQa4+v7/dv/sj5oUsIWX+YuOsh/NP1W4qO+YTSs0zX98kes8zX95WJv4DeQPXtE1A6qHlw6hrHPppoMYHEJW82wv86k+p1eFupOXB74cKFVo/f+eyzzxg4cKDFurNnz9K5c2ebx/j8888B02f4E87OprFtLxuXBBAbG8vnn3+ORqOha9euKYpdYTQa38Wux/+ctTnS7tk3/xZPQ8rHKb2LohUZ47vDKU06n3sLDKnz6mQyPXD5Nm2fDfVvuFWrv71DSBXXol78zK/0pGlY2n1ZAhiW75NUO9bEqz9btRw5Ojri6Oj4gj2sXbp0iQ8//JBTp06ZZ6HHxsZSoUIFNm/eTNGitmfX3rx5k0GDBuHp6cmcOXPInDmzzXIv8p/rbhNCCCHEv8fR0RE3NzeLJSUJEkD+/PlxcHDg+vXr5nU3btzAwcGBfPny2dxn//79tG3bllq1arF06dIUJ0ggSZIQQgghnvOuDdx2dnamSZMmzJgxg6ioKKKiopgxYwbNmjVDo7F+RMmZM2cYMGAAI0aM4H//+5/5Z8pSSpIkIYQQQlgwYEy1JbWMGzeOfPny0bx5cxo3bkyuXLkYO3aseXvTpk1ZsGABAAsWLCA5OZlJkyZRrlw589KzZ88XHd6mjDH4QgghhBCp5l0crOzm5oa/vz/+/v42t2/fvt38/0+SpbclLUlCCCGEEDZIS5IQQgghLKTWWKL0TpIkIYQQQlgwvpMdbv8+6W4TQgghhLBBWpKEEEIIYUG620wkSRJCCCGEhdScup+eSXebEEIIIYQN0pIkhBBCCAvSjmQiSZIQQgghLEh3m4l0twkhhBBC2CAtSUIIIYSwILPbTCRJEkIIIYQFeZikiSRJQgghhLAgLUkmMiZJCCGEEMIGaUl6R6iM6b9ps6BvlL1DSBXv331k7xBSxU9Jue0dwlu7tc/V3iGkCpda/e0dwlvLf3C+vUNIFTknD7Z3COmCdLeZSJIkhBBCCAvS3WYi3W1CCCGEEDZIS5IQQgghLBgywBCQ1CBJkhBCCCEsSIpkIt1tQgghhBA2SEuSEEIIISzIb7eZSJIkhBBCCAvyCAAT6W4TQgghhLBBWpKEEEIIYUGek2QiSZIQQgghLMiYJBNJkoQQQghhQcYkmciYJCGEEEIIG6QlSQghhBAWZEySiSRJQgghhLBglJ8lAaS7TQghhBDCJmlJEkIIIYQFmd1mIkmSEEIIISzImCST/2SSVL9+fe7fv49abVn9cuXKsWzZMjtF9WpOXu6Um94Tr+rFMCYbCFp/iPPjV2HUW5/O2RuUpeTo9rjmzUb83Ugu+K/m3p+nrcqV9u+Mg7szJz9faF6XuUReSo3/lCyl82PQ6Qnbc4ZzY1eQ9CA21eukzOKB5+gv0FQsgzFZT9zvu3kwayHYqJPbR81w7/gRKm9P9BFRRK/eQOzaLU+3t2mOe6c2qDyzkhx8j4fzlpBw8Fiqx/witRtU56sxA8mV15fQ4HtMHz+HfX8eslnW0zsrhy/uJC4u3rzuYeRDGlRsCUCTlg2Z/sMEEhOTzNt379jH/waMS7P41Z6ZKTC9L+7VSmLU64lYf4A7E36y+V48kfWDquQZ05kz1fqb1ymcHMgzshNZm1ZD5aZBez2YwMkrif77QprF/iy1Z2ZyTxmAW9WSGPUGHmzcR/CkZS+tR+Ym1cg5shuXa/W2WO/5aROy9WqF2tuDpKAwQqcuJ3rPP2ldBQBUWTOTffznOFcuDXo90Vv3cH/aYpv1yPzxB2Tp0hp1tqwk34/iwfJNPPplGwAKRwe8hnTHrVFNlK4uJN0MImLmMhKOn/tX6pESUQ8e0rHPl4wfPpjK5UvbOxwzhVtmnNoOQFWoJOgN6E7tI2nLMjC8+JxS+uTBefC3aBePR3/j8bnv4IhTyx6oSlZFoXZAf/cGSZuXYgi9/e9URKTYf3ZM0vjx4zl9+rTF8i4nSACVFw4kOU7L72UHsLfJGLxrl6RQnw+syrnm96HqksFcmrqOrYV7cnnGOiovHITGJ4u5jGMWNyrO60+hXo0t9lU4qKi+ahgRhy+xrVhvdlX7Ak32LJQa/2ma1Mn7m9EYEhK4+/7H3Ov8GZrK5XHv2MaqnHPd6ngM7EHEuGkE1WpBxLipeAzohkv9WqY6N2uIR+9ORIycTFDN5jxathrv6eNQeXmmSdzPy5s/N3OWTWX21AVUKlSPudMWMWvxFLL5eNssX6psce7eCaZC/jrm5UmCBFCqXHG2rP3dYntaJkgAhRd8iT5Oy6nyPbjwwf/IXKs0OXo3t1lWoVaRo38rCv3wJQql5W0kz8hOZKpUlIsthvNP8S6Er95NkeUjcfT1StP4n8j7/VD08VouVO7K1RZDcKtZBu+eLW0XVqvI1qc1+eYORaFQWGzK8lF9fD5vz51BMzhf/GPCvl9HvgUjUGfL+i/UAnLMHIkhXsvNOh0JbPc5LtXKkaVLa6tyrg2q4fVFN+6NmMH1iq25N+JbvD7vglvDGgB4DemOc7kSBH3yBTeqtiV6/R/4/jABdQ7b56a9nDp3kY59viQoONTeoVhx6jwUY5KWuK+7Ev/dEFSFy+BQ5wXnFJiSoU5foXB0sljt+P4nKLx9iZ82gLhxnTGE3ELTbUQaR/9mjKn4X2qJj49nxIgRVKlShQoVKjBs2DDi4uJeuV94eDjVq1dnw4YNKf6b/9kk6UXCwsIYPHgw9evXp0yZMjRo0IB169aZtxcpUoSJEydSpUoV+vbtC8Dff/9NmzZtqFixIk2bNmXLli0vOvwbc82XHe8aJbjgvxp9QhLxgeEEzNpIwe4NrcrmbVeLiGMBhP7xD0a9geAtx4g4epn8neoDoHJxouGhb9FFxxO8zbKlxajTs6v6lwR8twmj3oCDhysqFycSI6NTvU7q3DnRVCrLw+8WY9QmkhwcyqMlq8j0sfXNR+XtRfSPv5J0/jIASecuk/jPGZzKlwLAvVM7Hv7wE0kXrwAQv3Mv97oOwvAaF1BqaPVxU04eO8Nfv+9Hr9fzx5bdnDhyio87f2izfKlyxblw9vILj1eq7Mu3pzanfD5krlGKwInLMSQkkRgYRvB3a/HpZp2EAxT9ZSyZq5ckZN5Gq21KjSN3p/9CUkgkGAyEr96NMVGHa6mCaV0NHPPmIFO10oRM/gmjNomkoDDC5qzBu3NTm+ULrhyPW/VShP2w3mpbtt6tCP12FfFnrwHwcMsBrrUeiiE23qpsanPIkwOXKmW4P2MJRm0iurv3iPxhNR4drZNWdTZPopb8hvZsAADaM5eJP34O54qma0Ph5ETE3OUk34sAg4FHa//AmKRDU6JwmtfjdW3e8Sf/+3oag3p3sXcoVhReOVAXKk3S1p9Al4QxKgzdn2twqGH7nAJw+qgf+vNHrdYrs+V6nIw/TsiNBoy6xLQJ/C0ZMKbaklr8/f0JDQ1l586d7Nq1i9DQUGbMmPHyehgMfPXVVzx48OCN/uZ/srvtZUaPHo2Hhwfbt2/H0dGR5cuX4+/vT5MmTXB1dQUgMDCQffv2odPpCAgIoF+/fkyfPp0GDRpw9uxZ+vfvT5YsWahVq1aqxeVeJBeJUTFowx6a18VcCcYllzcO7i7oouMtykYHBFnsH3M1mMzF8wJgSNSxu85QEiOiqTC7j9Xf0sebLto6W8bhWbkI0Vfucu37balWlyccCuRD/zAafUSkeZ3u5h3UObKjcHPFGPs0wXm2Ww1M3XRO5UoTP3MBCo0TDgXzYjQYyL5kJg4F8qG7E8TDOYsxJmhTPW5bChUtwNXLNyzW3bh6iyIv+CAqWbY4mT3c2bL/V7y8s3L+zCWmfT2bG1dvoVAoKF66CPHxCfQY0AmVSsmB3X8zw38u0Y9i0iR+lyK50UXFoAt7eiOJvxqEUy5vVO4u6KMtE4Mbg+aQFBqJd7t6Vse69b8FFq/da5RE5e5C/MVbaRL7szR+eUh+EE1yeJR5nfZaEI65sqFyd0UfbZk0Bw6ehe5eJFnb1LdYr9A4ovHLAwYDhX6bgsYvN4k3gwmZ8jOG+LQ/pxwL5TVdG/ef1iPpeiAOObOjzOSKIeZpPZ50qz2hypoZl4olCZ+6CIDwr+dYbHeuUgZlJhe0z52v9lSjSgWaNqqPWq1i6Lhv7B2OBWX2PBjjojFGP30vDGFBKLNmA40raC3PKXXFeii9cpCwZi6OjdpbbEvavwnnLiNwm7gKo16PMS6ahPmj/pV6pHcJCQls3bqV5cuX4+HhAcBXX31F586dGTZsGM7Ozjb3+/777/Hx8SFHjhxv9Hf/sy1J48ePp2LFihZLfHw8EydOZNy4cTg4OBASEoKrqytarZZHjx6Z923WrBnOzs64u7vz66+/0qBBAxo1aoRKpaJ8+fK0a9eOVatWpWq8ajeNOXl5Qp9geq121TxX1pnk58vGJ5nLGfUGEiNe3TJ0sN1kthbpxaPLQdRcOxKUilfukxJKV2erJMaoNb1Wutg+4QGUnlnINm8ySQHXiPvjL5SZ3FAolbh3akvU5NncbdSO+D/2kG3uFFQ5sqdqzC/i6uZKfHyCxbqEeC0uri42y8c8iuHksTN0/rAPDSu14vaNQJatnYdbJleyemXh0vkr7Nz6F01rtuWTpj3IWyA30+ZPSLP4Va7OGJ57LwyPzy+Vq/V7kRQaabXOFrfyfhReOJS73/5GYlD42wf6Cio3ZwzPnftP6qF00ViV192zXQ91ZtM5la13K+6Oms/FSl15sPkABX4eh2OubKkf+HOUri5WyZhR+6QeL742VF5Z8F00Ee3F68Rs22u1XVOmKDm/G0Xk96tIDg5L3aDfgpdnVtRqlb3DsEmhccaYZHlOPXmtcLI8pxTZfHFs8inalTPAaD1eSaFUkXz+b+LGdyVudAf0F47i3H0UqB3SrgJvyGg0ptqSlJREbGysxZKUlGT1N7VaLXfu3HnhotPp8PPzM5cvWLAgWq2W27dv26zD0aNH2b59O+PGvflQhf9sS9K4ceNo3dq6f//SpUtMmzaN27dvky9fPvLmfdz68swAvWzZnt4kg4ODOXr0KBUrVjSv0+v15MmTJ1Xj1ccnonK27N9+8loXa/nhnByvReXsaFnWxZHk58q9ikGrw6DVcW70zzS9sIDMxfPw6MKdN4j+BcdP0KLQWNZJoTHddAzxtmN1LFUM76ljSDx9gYivp4PegFGnAyBm5Tp0N03xxazZjFub5jjXrGLVCpUa+nzeld6Du5lfnzt5EWdnyxums4uGuFjb3X1f9Rtj8fqbsbP4qENzKlYtx74/D9Gp5dMWvtDgMKZPmMtvf/yIq6uLxWDv1KKPT0T53Pn15LU+hefNE94d3iPf+G4ETf+Ve4u2vnWMr8MQr31xPeJevx6GJNM5Fb5kM9prplbZiJ+34/lpEzLVq0Dkit9TKWLbjAnW9XhyrRhe8P5ryhQlx6xRJJy8QNjImVYDvN3bNCbb8D5EzF3Bw59TPjbjv8qYpEXh8Nx78XiskTHxmXNK7YCm8zASNy/B+DDC+kBKFZou/yNh8QSMj0ytUokbFuE66RdUfmXRXzqRZnV4E6k5u23hwoXMmzfPYt1nn33GwIEDLdadPXuWzp072zzG559/DoCLy9Mvnk9aj2yNS4qMjGTkyJHMmTPH3Av0Jv6zSZItOp2OPn368OWXX9KhQwcUCgUXLlywGmP07ABPHx8fPvzwQyZMePotPzw8PNWfVvoo4C5Onplw8nI3twJlKuJLfHAkyTGWN//ogLt4lM5nsS6Tny8Pz7y6u8Mltxe11o9mf7Ov0YY/BEDpaDpNkh6m7vge3fXbqLJkRpnVA0OU6W85FMhL8r1wi662J1xbNibrsAE8/OFnYlY+HSdmeBiNPvIBOFp+G1Moleau/9S2cPZPLJz9k/n14BH9KF66qEWZgn75uXDGelyRq6sLA4b2YuWSNYTcvQeASqVErVaj1SbiV7wQzVo3ZubEpzcVRycHDAYDSY8TwtSWcCUQh6zuOHhlRhdhajV18ctNYkgE+pgUJmVKJfmn9CbrB1W40n0q0Qf/vVlUCVfuoM7qjtrLg+SIhwBoCucmKeQ+hhTUQ/8gBt39hyhtnFPPD/BOC4nXTNeGytMDfeRDABwL5UEXet/mmCj31o3INqo/kXOX8+Cn5xIgpZJsYz/DrWF1QgZOIP6I9SxX8WKG0Dso3NxRuHlgjH0IgDJ7bgwP7oP26XuhzF0YpXdONB8PhI+ffvhreo4h+Z+9JO5YgcIlEzw7q9poMC365H+rOq8tNQdc9+nTh27dulmsc3R0tCpXpUoVrly5YvMYly5dYvbs2SQkJJiTnoQE02efm5ubRVmj0ciwYcPo1KkTJUuWfKvY/7PdbbbodDq0Wi0ajQaFQkFISAjTp083b7OlTZs2bNu2jUOHDmEwGLh9+zaffvppqs+Ui7t1j4ijAZT274zaVYNLHm+KfvEhd37ZZ1U2cN0hvKsVx7dFFRQqJb4tquBdrTiB6w6+8u/EB0WQ9CCOUhM6oXJxwjFrJsp+0517f50h4a6Nb0dvITkoGO3p82T9qj8KF2fUOX3I3LMjsZv/sCrrUr8WniMGcf+r8RYJ0hMx67fi0asTDn4FQaUkU/tWqLJ5kbD3cKrG/CKb1+6gcvXyNG7xHiqVisYt3qNy9fJsWbvDqmxcXDzValdm2Nef45bJFRdXZ8Z8M4y7gSH8c+QUjx5E07FHW3p81gmVSkUO3+wMGzeIjb9uQ5eUNkmS9lYo0ccukXdCd5SuGpxyZ8N3cFvu//JXio+Vd3w3POqX43zjYf9qggSQdDuU2OMX8R3bE6WrM465s5N90MdErdmd4mNFrvqd7IPa41w8P6iUeHVthoNPVh7ttB6Qm9p0d0KI/+cC3iP6mq4N3+x49utA9PqdVmXdGtYg27jPCBnkb50gAd7D++BaqyKBbQdJgvQGjBGh6G9exLFVT3ByRpE1Ow4NPyb5uOU5Zbh1ibj/tSVuVAfzAqBd4k/i+gWQEIf+5kWcmnVF4ZYZ1A44NuuCMS4G/a1L9qjav8bR0RE3NzeLxVaS9DL58+fHwcGB69evm9fduHEDBwcH8uXLZ1E2NDSU48eP8/3335uH04SEhDB+/Hj69LEeh/sykiQ9w8XFhcmTJ/P9999Trlw5OnfuTI0aNfDy8uLq1as29ylTpgwzZ85k5syZVKpUiU8//ZT69eszZMiQVI/vWM/vUKhVvH98NnV3TCBs71kuzzTdFFvcWEbu1qYpv7HXQzjSbSZFBrWk2ZXFFP2yNUd7fkfszXuv9XeOdv0WpVpF43/m0OCvKcQHR3C879xUrw/A/aHjQaXCd+tKfJbPJeHvEzxavBKA3Ie24trENKA2c59OoFLhPX0cuQ9tNS9ZR5qaYB8tXMGjn9fg/c1ocu/fjGvThoQPGon+/uuNnXlbt67f4bOuQ+kzuBvHr/1F/yE9GdT9f9y+GQhAs48ac/LWfnP5AZ2HoFSp+PP4Jg6c24GXtye9P/mc5GQ9YaHh9O3wBe81qcuxq7tZt2s5509fwn/E9DStw7Ve01GoVJQ7toCS26fycN9p7s5aC0Cla6vw/LD2K4+hzpoJn66NcfD2oMy+76h0bZV5eZ39U8PtflNRqFUUP7SYwpumE7P/FPfmrAGg1KU1ZGlV57WOc++7XwlfuIG884ZS6vwvZG1dj5tdJ6ALi3r1zqkgdPBEFCoVBXb/RJ41s4k7+A+RP6wGoNA/G8nUzDRo3nPApyhUKnLOHk2hfzaal2zjBqL0cMejQzPUXlnIt2WhxfYn+4tX0/40FYVSheuoxTh/Ph19wCmSdpnOKdcpa1CXf71zSvvTVAzhwTh/NQfXcT+izJ6bhEXjIOndm+H2rs1uc3Z2pkmTJsyYMYOoqCiioqKYMWMGzZo1Q6OxHOqQM2dOzp8/zz///GNecubMybhx41i4cOEL/oJtCqP8it07YYNPB3uH8NYq5Ez7gbn/hvfvPnp1oXTgJ3Vue4fw1jQO7143xJtwcbEepJre5D84394hpIrEyYPtHUKqcJuZ+mMtn9UgV6NUO9Zfd3elynFiY2OZOnUqe/bsQafT0aBBA8aMGWMep9S0aVOaN29ufjzPs+rXr89nn31mcyzyy8iYJCGEEEK889zc3PD398ff39/m9u3bt79w3z179rzR35QkSQghhBAW5AduTSRJEkIIIYSF1Jzdlp7JwG0hhBBCCBukJUkIIYQQFgwypwuQJEkIIYQQz5EUyUS624QQQgghbJCWJCGEEEJYkNltJpIkCSGEEMKCJEkmkiQJIYQQwoL8GIeJjEkSQgghhLBBWpKEEEIIYUG620wkSRJCCCGEBXnitol0twkhhBBC2CAtSUIIIYSwIAO3TSRJEkIIIYQFGZNkIt1tQgghhBA2SEuSEEIIISxId5uJJEnviIzQpKfJrLN3CKni+oUQe4eQKnzLuNs7hLf2V0gOe4eQKryj9PYO4a3lnDzY3iGkCqeR39k7hHRButtMMsJnsxBCCCFEqpOWJCGEEEJYkOckmUiSJIQQQggLBhmTBEiSJIQQQojnSEuSiYxJEkIIIYSwQVqShBBCCGFButtMJEkSQgghhAXpbjOR7jYhhBBCCBukJUkIIYQQFqS7zUSSJCGEEEJYkO42E+luE0IIIYSwQVqShBBCCGFButtMJEkSQgghhAXpbjOR7jYhhBBCCBukJUkIIYQQFoxGg71DeCdIS5IQQgghLBgwptqSWuLj4xkxYgRVqlShQoUKDBs2jLi4uBeWDwgIoEuXLpQrV47q1aszZcoUkpOTU/Q3JUkSQgghhAWj0ZhqS2rx9/cnNDSUnTt3smvXLkJDQ5kxY4bNslFRUXTt2pXq1atz/PhxfvvtN/bt28fPP/+cor+ZYbvbbt26xYIFCzhy5AgxMTF4enrSuHFj+vXrh6ur60v3PXbsGJ07d+bKlSs2ty9YsIB//vmHJUuWpEXor83Ry51y03viVb0YhmQDd9cf4sL4VRj11s2k2RuUpfjo9rjmzUbC3Ugu+K8m7M/TACidHCgxqj05m1VG7eZMzPUQLk36lYjDl9K8DgoPD9yHfIVj2bKg15Pw55/E/vADGPTPFVTg2rkLzh80RZHJDX3oPeJWLCdx317TdgdH3Hr3RlOnDgpnF/RBgcQsWoTuzOk0r8MTTRrXZ/LkkRTIn5fAoGCGD5/I9h27rcrlzp2T82f3WaxTqZQ4OztTs1YLjh47Sb26NZg0cThFixYmPj6Bdeu3MXzEJLRabZrFr8ziQZaRX6KpUAajXk/877t5OHsB2DifXFs3I1OHNqi8PNFHRBH763pi120BwHf/tucOrECp0RA5aiLxu/amWfzP861fhooj2+OW15u44Ej+mfgLd3efsVlWoVRQYWR7CrapidrZkdDDlzgy/EcSwh9alXt/zUhi797n0BeLUjVeRy93Ss3oiWf14hiTDQSvP8Tlr1favJ69G5Sl6JhPcMmbDe3dSC5PWEX44+tZndmVkpO74l2vDApHFY/O3OTyuJVEX7xDlipFqPzLcMs6qVWonBzYXbo/iWEPUrVOCrfMOLUdgKpQSdAb0J3aR9KWZWB4cVeO0icPzoO/Rbt4PPobF0wrHRxxatkDVcmqKNQO6O/eIGnzUgyht1M13tQQ9eAhHft8yfjhg6lcvrS9w3knJCUlkZSUZLHO0dERR0fH1z5GQkICW7duZfny5Xh4eADw1Vdf0blzZ4YNG4azs7NF+U2bNpEvXz769OkDQK5cuVi2bBkKhSJFsWfIlqRTp07x4Ycf4uvry6ZNmzh9+jSLFy/m7NmzdO/eHb1e/+qDvETfvn3tniABVFo4kOQ4LX+UHcD+JmPwrl2Sgn0+sCrnmt+HyksGEzB1HdsL9+TyjHVUWjgIjU8WAEqMak/Wyn4caDaO7UV7cWfVXqqu+ApnX880r4PH2K8xJiRwv81HRPXri1OFCri0bWtVzrnVh2gavc+DLz7n/gdNiF28iMyjx6DKmRMAt969cSxZiqgB/bnfsjkJ27eTZfIUlNmypXkdAAoVys9vaxYx7uvpZPUqyvgJ3/LL6gXkzOljVTYoKASPrH7mxdO7GEeOnGTpstUcPXYSL6+sbNn8MwsWrsDTuxgVK79PnTrV+N+wAWlaB8/JYzAmJBDSpB1hXQegqVyeTJ+0sSrnXKcGHgN6EvX1VILrNidq/FQy9+uOc71aAATXaWaxJPx1gIQjx4n/a3+axv+sTPmzU2/R55yevo7VRXtz5tsN1F0wEJfH5/zzSn/eipx1SrL1gzH8VmEQem0S1Wf0tCpX5svWZKtSJE1iLr9oEPq4RHaX6c+hJqPxql2S/DauZ5f8PlRY+gVXp65lV6EeXJ2+jvKLPsfpcd1Kz+yFOpMze6sO5s+ivXl4+gYVfh4CwINjV9hZoJt52V2qH/G3wrjyzW+pniABOHUeijFJS9zXXYn/bgiqwmVwqNPyxTs4OOLU6SsUjk4Wqx3f/wSFty/x0wYQN64zhpBbaLqNSPV439apcxfp2OdLgoJD7R3KW0vN7raFCxdSoUIFi2XhwoVWf1Or1XLnzp0XLjqdDj8/P3P5ggULotVquX37ttWxzp07h5+fH2PHjqVGjRq89957bNmyBR8f63vyy2TIJGns2LG0atWKQYMGkTVrVgDy58/PrFmz8PT0JCgoiFOnTtG5c2dq1qxJqVKlaN26NWfOnLE4zqJFi6hTpw61a9dm+vTp5kx47ty5dOrUCYANGzbwySefMHHiRKpWrUq1atUYNWoUOp0uTevomi873jVKcNF/NfqEJOIDw7kyayMFuje0KpunXS0ijwUQ+sc/GPUGQrYcI/LoZfJ1qg+AUuPI5WnrSAiJAoORO6v2YkhMxqN0/jStgyqnL47lyhG7cAEkJqIPDSV2xXJcWn1oVTZh00Yie3RDHxICDg4oPTwwarUYH7esKJyciP1xGYb798FgIGH7Now6HQ5+afOB9rzOndpy6NBxtmzZiV6vZ926rRw4cIRePTu+ct9RIz8nW3YvBg4aBUBERBQ5fMuwfMVvGI1GPD2zoHHScP9+ZJrFr86VE03FsjycswhjYiL64FAeLV2JW7tWVmVV3p5E//wrSRcuA5B0/hLak2dwsvGt2aXZ+2iqVCBqzBSbLVJppVDbWoQdv0LgzpMY9QZubz3GvSMB+HWsZ7O8X4e6nP9+G/EhUehiEzg2dgW56pXGLY+3uYxPjeLk/aASd3acSPV4XfJlx7NGCS5PWI0hIYmEO+Fcm7mBvN0bWZXN9XFtoo4FEPa76XoO3XKUyCOXydOpAQCn+8zlVK/ZJEfHo3LV4ODuQlJkjM2/W2JyV7T3org+a2Oq10nhlQN1odIkbf0JdEkYo8LQ/bkGhxpNX7iP00f90J8/arVemS3X4xaAx60ARgNGXWKqx/w2Nu/4k/99PY1BvbvYO5RUkZrdbX369OHkyZMWy5MWnmedPXuWRo0a2Vz27NkDgIuLi7n8k9YjW+OSHj16xIYNGyhdujT79u1j3rx5rFmzhh9//DFF/w4ZLkkKDAzk2rVrNGvWzGqbl5cX8+fPx8fHh379+vH+++9z4MABjh07Rp48eZg2bZpF+atXr7Jjxw5WrFjBrl27WLx4sc2/eerUKTw9PTl48CALFy5kx44d7Nq1K03q90SmIrlIiopBG/bQvC7mSjAuubxxcHexKhsdEGSxLuZqMJmL5wXg7LClhO85a97mVaM4Du4uPLp4J+0qAKjz58Pw6BGGyKcf/vrbd1D5+KBwdbMsbDSCVotjxYpk+30n7kOHEfvjUgxRUab6zPyWpOPHzMUdypVD4eqK7vq1NK3DE8WL+3HhQoDFusuXr1G6dPGX7legQF6GDR1Anz5DLZqjY2NNF/3tm/9w9vQe7t0L46ef16R+4I+pC+RD/zAaQ8TT9yL55h3UObKjcLPsno5dt4WY5b+aXyuzeOBUrjRJl69alFO4uuLxeV8ezJyP4VF0msVui4dfLh48d84/vBZM1uJ5rMo6ZHLGNaenRXltRDSJj+LIWsxUXuPpTo0ZPTnw2Xz0Can/4ZypqOl6frY1J/ZqMC65vVHbuJ5jLlvWLfZqMO4lTLEak/UYEnUUGdGORgGLyNm6BpfGLLf6m1mqFCFny2qcH2L7vva2lNnzYIyLxhgdZV5nCAtCmTUbaKyHPKgr1kPplYOknb9abUvavwmlT17cJq7C9Zu1qCvUQ/vzNKty9lSjSgV+/20ZTd6rY+9Q3jmOjo64ublZLLa62qpUqcKVK1dsLnXr1gVM3W5PPPl/Nzc3q2M5OjpSqlQp2rRpg4ODA0WLFuXTTz/l999/T1HsGS5Jinr8oenl5fXCMg4ODqxZs4YOHTqQlJREcHAwHh4ehIWFmcsoFArGjh2Lq6srefPmpWfPnmzZssXm8TQaDX379sXBwYHSpUtTpEgRbt26lboVe47aTUNyvOXNOvnxzVvlqnmurLNVWX18klU5gCzlC1F58ecEzFhPfOD9VI7aksLZxdwS9IQx8XHL0HP9y08knT1LeKOGPBw6BLfuPXGqZ90y4FCsOB7jxhP3808Y7t1L/cBtyOTmRlx8vMW6+IQE3F4x/m34/wby+x97OHb8lM3tRYvXJHfe8uj1Bn77NXXHwDxL6eqCUZtgsc7w+L1Ruth+LwCUnlnwnj2FpMtXid/5l8W2TO0/RB96j4Q/96V6vK/iYOP60CckobZxzju4mer3wvIKBbXm9uPiot95cCkwTeJVuzqjt7pGEx9ve/561qCPt7xu9AmJVuWuzdrIH3m7cG3Geir/OhznvJZdz35D23Dn5z9JuBuRWtWwoNA4Y0yyrNOT1wony1gV2XxxbPIp2pUzwMbUc4VSRfL5v4kb35W40R3QXziKc/dRoHZIk9jfhJdnVtRqlb3DSDUGozHVltSQP39+HBwcuH79unndjRs3cHBwIF++fFblCxYsaDUOymAwpHggeYZLkry9Tc3j9+/b/oCPiIhApVJx7NgxGjVqRIMGDZg4cSJBQUEW/3ju7u64u7ubX+fIkcMiiXqWp6enxWAwBweHVB3Rb4s+PhGVs2W/vfrx6+TYhOfKalE5W2btKhdHq3J5O9SlxtqRXJm9mStp0Pz+PKNWi0JjWYcnN09jQrytXUCnA4OepFOn0P65C02D9yw2O3/QFI9vvyVu1UriVlh/e04tw/83kIdRV82LQqHA5bnEzsXZmZjY2Bcew9XVhfYft2Lu3KUvLKPVagkNDWPEyEk0blwfD4/MqVaHZxkTElBoLD+4lI9fG+ISbO2CY8liZP95Pro7QUQMGWPVneba8gNi1qT9eQRQamALOl5dYl5QKFA/f847O6KLtR74nvw44VA/dz2ZyidQemBz9Ik6An78M83iT47XWl3PKhfb13OyjWtf5exE8nN1M2h1GJKSubVwBwl3I/BpXMG8zSVvNjyrF+f2kj9SsxoWjElaFA7PXd+PxxoZE5+pk9oBTedhJG5egvGhjYRNqULT5X/ojv+F8VEUJCaQuGERisyeqPzKpln8/3XGVPwvNTg7O9OkSRNmzJhBVFQUUVFRzJgxg2bNmqHRWH/5+eijj7h69SqLFy9Gr9dz5coVVq5cScuWLxkTZ0OGS5J8fX3x8/Njx44dVtsiIyOpV68e8+bNw9/fn1mzZnH48GF+/vlnatSoYVE2NjaW+GdaBoKCgvD19U3z+F9XdMBdnDwz4eT1NJHLVMSX+OBIkmMSrMq6F8llsS6Tny8xAXdNL5QKykzrQfFR7TnWdSY3Flr/26WF5Fs3UWb2QJnl6WBaVb686MPDMT7Xx+zWrz9u/fpbrFM4OGCMfjzWQqkk05dDcOvVm0ejRxO/9rc0jf2bqXMtBl8fO36K4sX9LMoUK1aYixdtz5AEaNKkAffvR3LgoOUYjGpVK3Lh/H4cHJ5+S3ZyciQxMZG4uBckj29Jd+M2Ko/MKLM+fS/UBfKSHGb9XgC4Nm+M9/zpxP6ygagxk03J6zMcixdBmcWDhN3/zmDt83O3sMqvp3m5f+o6Hn6W57xHYV8eXrlrtW/So3jiQqPwKPL0+nb2zowmSyYeXrlLwY9q4lOtGB0uLaTDpYXkb1Wd/K2q0+GS9cDTNxUTcBdHz0w4ej9Ngt38fEmwcT3HBgTh9tz17ObnS8zj7sLq28bj06yyxXalkwNJD56+jz7NqhB1/AoJQWnTigRgCL2Dws0dhZvH0ziy58bw4D5on57HytyFUXrnRPPxQFwnrcZ10moAND3H4PRRX3DSoHDJBOpnJmMbDaZFn7Jn3oj0bdy4ceTLl4/mzZvTuHFjcuXKxdixY83bmzZtyoIFCwBTS9LKlSvZt28fVatWpWfPnrRv3948nvh1ZbgkCWDMmDGsX7+eefPm8eDBA4xGI5cvX6Zv376UKFGCMmXKoFQqzdnnmTNnWL58uUXTnF6v55tvviE+Pp4bN26wdOlS2rdvb68qWYm7dY+IowGU8u+M2lWDSx5vinzxIYG/7LMqG7TuEF7VipOzRRUUKiU5W1TBq1pxAtcdBKDUhE5kr1+Gfe+P5v7BC/9aHfTBwSSdO0emAQNRODuj9PHBrVNnEnZstyqrO3cWl+YtcChdGhQKHKtVR1OvPgnbtwKQacBnOFWuQmTf3iSdOvmv1eGJlavWUadONdq0aY5KpaJNm+bUqVONlavWv3CfGtUrcejwMav1585fwsXZmSmTRuLg4ECePL5MmzqGZT/+mmYTApKDgkk8fR6PL/ujcHFGldOHzD0+JW6zdf+9c71aZBn+OZHDviZm1Vqbx3MsWwpdwFWMifYZXHtz3SF8qhUjX3PTOZ+veRV8qhXjxvpDNstfX3OAMoNa4ZbbG7WrhsrjP+Xe35eJuRPOxjrDWF20F6uL92F18T7c2vQ3tzb9zeri1gNP31T8rXtEHQ2guH9nVK4anPN4U/jL1gSttn5kwt21h/CsXpwcLaqiUCnJ0aIqntWLE7zWdD0/OHUdv6Ftcc7lhdJRTeGhbVA6qgnb+fS6yFqlCFFHA6yOnZqMEaHob17EsVVPcHJGkTU7Dg0/Jvm45WMxDLcuEfe/tsSN6mBeALRL/ElcvwAS4tDfvIhTs64o3DKD2gHHZl0wxsWgv5X2jyn5r3oXn5Pk5uaGv78/hw8f5vjx40yZMsViIPf27dvp27ev+XWZMmVYtWoVJ06c4ODBg/Tv3z/FjwDIkM9Jqly5MitXrmTBggU0bdqUhIQEvLy8aNy4MX369MHV1ZUOHTrQsWNHDAYDuXLlolOnTnz77bdERJi+WXl4eODh4UGdOnVwdXWlffv2dOz46plK/6YTPb+j9JRuNDw+G4wGAtceJGDmBgCa3VjGmaFLubvhMLHXQzjWbSYlRren3MzeJNyN4HjP74i7eQ/HrJko0K0RRr2BBvstB0I+2T8tPfp6LJkGDcbrl1/BYCRh105zN5n3jt+Jmfkt2t27STx8mOi5c3D/ahjKLFnQ373Lw3Fj0F28iMI9M84tW4HBgOePP1kc/8n+ae3KlRt81KYHkyePYvHCGdwJvEu7j3tz7dpNAD755EN++H4qHlmftjYVyJ+XS5etW5ri4uJp2rwjM2eMJ+TuGR49imH1LxuYOOm7NK1DxPDxZBk6kBybV4HBQNyOP4leuhIwPfvowZRZxP/xF+69OoNKhefUry32j/99Nw++McWo9s2B/n7atVK8yqMboezpMYuKo9pTY0ZPYu9GsLf3bKJvmsaoFfiwOtWmdmeVn2ma/5lZG1GqVTTZOAYHVw33/r7Evr5z/9WYT/b8jpKTu1L/xByMRgPBvx3k2uPr+f2bP3J+6BJC1h8m7noI/3T9lqJjPqH0LNP1fLLHLOIe1+3KxF9Ab6D69gkoHdQ8OHWNYx9NJPnR05Ykl7zZCP/rTJrXSfvTVJxa98F11GKMRgPJ/+wlaZdpAoLrlDUkrp1P8qlXtzZqf5qKY/OuOH81B4VKhf7OFRIWjYOkd2uGW0aSmk/KTs8UxrQePCNeyyafDvYO4a1VKxZs7xBShe/h668ulA7cKlPU3iG8tb9Cctg7hFThnfx2z2Z7F9Tp+OKff0hPnEZ+Z+8QUoWDV4E0Pb535tR7fMr9Ry8edvCuy5AtSUIIIYR4c9J+YiJJkhBCCCEspNbU/fROkiQhhBBCWJCWJJMMObtNCCGEEOJtSUuSEEIIISzI7DYTSZKEEEIIYUG620yku00IIYQQwgZpSRJCCCGEBZndZiJJkhBCCCEspNYP06Z30t0mhBBCCGGDtCQJIYQQwoJ0t5lIkiSEEEIICzK7zUS624QQQgghbJCWJCGEEEJYkIHbJpIkCSGEEMKCdLeZSJIkhBBCCAuSJJnImCQhhBBCCBukJUkIIYQQFqQdyURhlDY1IYQQQggr0t0mhBBCCGGDJElCCCGEEDZIkiSEEEIIYYMkSUIIIYQQNkiSJIQQQghhgyRJQgghhBA2SJIkhBBCCGGDJElCCCGEEDZIkiSEEEIIYYMkSUIIIYQQNkiSJIRIkaioKHuHIIQQ/wpJkoQQr5ScnMysWbOoUKEC9evXJygoiI8++ojw8HB7hyaEEGlGbe8AhLDlxIkTryxTqVKlfyESATB37lyOHj3K7Nmz+eKLL/D09MTHx4dJkyYxe/Zse4eXYo8ePWLv3r2Eh4eTM2dO6tati5ubm73DEumQXq9n586d3L59G4PBYLHts88+s1NUIrUojEaj0d5BCPG8okWLAqBQKMzrMmfOTExMDAaDAQ8PD44cOWKv8N6IXq9HpVIBsH//frJkyULp0qXtHNXrqV+/Pr/88gvZs2encuXKHD9+nOjoaBo2bMixY8fsHV6KnDx5kn79+uHs7IyPjw8hISEYjUZ+/PFHChcubO/wUuTs2bMEBgai1+st1rdq1co+Ab2Bw4cPs2LFCsLDw1m4cCHLli1jyJAhqNXp4zv86NGj2b59O0WLFrWIWaFQsHz5cjtGJlJD+jgLRYoULVrUIrmw5fLly/9SNG8mICAAgKVLl3L16lVGjx5NpkyZiI+P55tvviFz5sx2jjBl9uzZw+jRo/n777+ZP38+CxYsQKFQMGrUKNq1a2fv8F4pPj6erFmzAvDke5VGo0GpTH899pMnT6Z79+707dsXMNVn3rx5TJgwgRUrVtg5utc3a9YsFi1ahJeXFw4ODub1CoUi3SRJW7duZcqUKbRt29bcerxnzx4UCgXDhg2zc3SvZ+/evSxfvpxSpUrZOxSRFowiwzl27Jjx2LFjxpkzZxpbtWpl3L17t/Hq1avG/fv3G9u1a2ecPXu2vUN8bdWqVTMmJCRYrNNqtcbKlSvbKaI306ZNG+OaNWuMer3eWK1aNeO+ffuM58+fN7733nv2Du219OnTxzhz5kyj0Wg0VqpUyWg0Go1Lliwx9urVy55hvZGyZcsadTqdxbqkpCRj+fLl7RTRm6latarx6NGj9g7jrTRr1sx4+vRpo9FoNFasWNFoNBqNt27dMtaqVcuOUaVM1apVjcnJyfYOQ6QRaUnKgCpXrgzAqFGjWLlyJdmzZwegcOHCFCtWjLZt2zJo0CB7hvjaDAYDkZGR+Pr6mtfdvXvX3G2VXgQGBtKuXTsuXbpEQkICNWrUQK1WExERYe/QXsuoUaPo0qULGzduJC4ujg8++IC4uDh+/PFHe4eWYvnz5+f06dMWY9quXbtGoUKF7BhVyqlUKqpUqWLvMN7KvXv3KFOmDPC0az1v3rzEx8fbM6wUadasGUuXLqV37972DkWkAUmSMrCoqCjc3d0t1jk5ORETE2OniFKuZcuW9OjRg549e5IjRw6CgoJYsmQJ7du3t3doKeLs7ExkZCR79uyhQoUKqNVqAgICyJIli71Dey25c+dm+/bt7N27l5CQEHx8fNLtYOcqVarQt29fPvroI/LmzUt4eDhr166lcuXKzJs3z1zuXR90W69ePbZt20azZs3sHcoby5cvH3/99Rfvvfeeed3ff/9N3rx57RhVyly8eJFTp07xww8/mLukn/jrr7/sFJVILTJwOwPr27cvjo6ODB06FB8fH4KCgvjmm29wc3Nj5syZ9g7vtSQnJ/P999+zZcsWwsLCyJEjB23btqVXr16vHHf1Lpk7dy6//fYb0dHRzJkzB09PT3r27En37t3TxTfQkJAQm+sdHBzInDkzjo6O/3JEb65Tp06vLPMuD7rt1KkTCoWCuLg4Ll++TKFChfDw8LAo867G/ry///6b/v3706BBA3bv3s2HH37Itm3b+Pbbb6lTp469w3stGzdufOG2Dz/88F+MRKQFSZIysPv37zN48GBOnjxpTihq1KjBzJkzrVqYRNo7duwYTk5OlC1bltDQUM6fP0+jRo3sHdZrKVGihNX05ieUSiXVq1dn6tSpVt+kRep7trXrRd71VrBnBQQEsGbNGoKDg/Hx8aFNmzbpZtanyPgkSfoPCA4OJjw8HB8fH3LkyGHvcFLs8OHDrFy5krCwsHQ5RfiJiIgIvLy8SEpKYt26dWTJkoUmTZrYO6zXsnLlSvbu3cvIkSPJnTs3d+/eZdq0aZQsWZJGjRrxww8/oFarmT59ur1DfanY2Fju379P/vz5AVi/fj2XL1+mYcOG6XJ8z40bN8iePTtubm6cPn0ad3d3ChYsaO+wXlu/fv2YPn16uuy27d27N4sWLTK37NmSXlr0xIulv/m7IsV8fX0pV65cukyQtm7dytChQ/Hz8+POnTuAaYpweukufGLt2rXmcRfTp0/n+++/Z9KkScyfP9/Okb2en3/+mW+//ZaCBQvi6OhIgQIFmDp1Kps2bcLPzw9/f38OHDhg7zBf6saNGzRq1IglS5YA8NNPP/H1118THh7OoEGDOHTokJ0jTJnff/+dVq1acfv2bQDOnDlD27Zt2b9/v30DS4HTp0+nq67aZ1WoUAEwjXGrXLmyzUWkf9KSlAFlhOckPdG8eXP8/f0pW7YslSpV4sSJE9y+fZvOnTu/8x/Kz2rZsiXDhg2jatWqVK5cmcWLF+Pt7U2nTp3Yt2+fvcN7pYoVK7J3714yZcpkXhcdHU2dOnU4ffo0er2eqlWrvtaT0u1l0KBB+Pj48L///Q+VSkXt2rXp2rUr3bt3Z//+/SxZsiRdPSepadOmDB8+nFq1apnXHTx4kOnTp7NlyxY7Rvb6Jk6cyN27d2nevDne3t4W9y15or54F6Sv/grxWjJSE29GmCIMEBoaSo0aNTh16hRqtZry5csDpkQjPahVqxZDhgxh1KhR5MyZk5CQEKZNm0aNGjVISkri+++/p0SJEvYO86X++ecfdu3ahUql4vbt29y/f5+GDRsCptaAIUOG2DnClAkNDbVIkABq1qzJF198YaeIUm7lypUAVl8UFApFuvkil5yczOLFi9m8eTNhYWHkypWL9u3b07FjR3uHJlKBJEkZ0JNm3iVLltChQwdcXFzsHNGbywhThMH0kyp37txh586d5vfn6NGjeHt72zmy1zNu3DiGDBnC+++/b05W69aty6RJk/jnn3/Yt2/fO98FqtVqzWNfzp49S9asWcmdOzdgGnz+/E97vOt8fX05ePCgRaJ05MgRcubMaceoUubJk/XTs++++45du3aZH1MSGBjIsmXLiIuLSxczV8XLSZKUgS1atIhu3brZO4y38sUXX5inCCcmJvL111+bpwinJ926daN58+YArFixgpMnT9KnTx/GjRtn58hej4eHB0uXLiUsLIx79+5hNBrZsGED9evX58yZM2zevNneIb6Sp6cnoaGh5MiRg6NHj1p05wQEBJAtWzY7RpdyvXv3ZsCAATRq1AhfX19CQkL4888/mTp1qr1DS5GEhAQePXpknj2p0+m4evWquZXvXbdt2zZWrFhhTrgBqlatSq9evSRJygAkScrAatWqxeLFi2ndunW6+wB4onr16vz666+sWbOGKlWqYDAYWLZsWbqbItyhQwdq1aqFWq0mR44cREVFsWrVKkqWLGnv0FIkKCiIpUuXsn//fgoXLszQoUPtHdJra9y4McOGDaNWrVps376dOXPmAHD9+nW++eYbi9bK9KB58+Zky5aNTZs2cfHiRXLkyMGyZcvMXbnpwfr16/H39ycxMdFivaenZ7pJkgCrFuGcOXMSGxtrp2hEapKB2xlY3bp1uXfvns1B3Omlv3/nzp00aNAg3U33tyW9fmM2GAz88ccf/Pjjj1y7do3k5GR++OEHq/Ew77qkpCT8/f05deoUTZs2pX///gCULl2akiVLsmjRonQ1FT09T59/omHDhnTs2BFXV1dOnDhBly5dmD59OjVq1KBXr172Du+1LF68mGvXrjF27Fjc3NzQarXmH+FOT+PDhG2SJGVgx48ff+G29DI9tXbt2uh0Olq1akWbNm3S1TNgnvWyb8zv8tTzn3/+meXLl2MwGPjkk09o164djRs3ZvPmzebfBEzvbty4kS7Pq6pVq3LgwIF0O4UeoGzZspw+fZrg4GC++uorfv31V0JCQujatSu7du2yd3gv9WQW8ZOPUKVSSaZMmYiLiyM5OZksWbJw5MgRO0cp3lb6/3ouXuhJIvTo0SOCgoIoXrw4ycnJ6eqmum/fPg4ePMimTZto3bo1xYoVo02bNnzwwQfpakD6ggULGDx4sM1vzO+yKVOm0KFDB4YPH56uzptXiY2NZf/+/ebZSL6+vmg0GnuHlSLNmjVj0KBB6Xr6vKenJzqdjhw5cnDr1i3A1FUVGRlp58heLSPNIhYvJi1JGVhcXBxjx45l+/btaDQaNmzYQLdu3fjxxx8pUKCAvcNLsZiYGHbs2MHChQt5+PAhp06dsndIry29fmNetWoVq1evJioqinbt2tGhQwdatWrFpk2b0m1L0vnz5+nZsycajQYfHx+Cg4NxdHRkyZIl6eq6KFq0qM316Wn6/BdffIGTkxNjxoyhe/futGrVCo1Gw7x589L9j8NGRUXJz/RkANKSlIFNmzaN+Ph4fv/9d9q1a0fu3LmpV68ekyZNYunSpfYOL0WCgoLYvHkzW7duRafTvdaPlL5L0us35o4dO9KxY0eOHDnCypUradiwIXq9niNHjtC8eXNUKpW9Q0yxKVOm0K1bN/r27QuA0Whkzpw5TJgwgZ9++sm+waVARpg+P2LECEaPHk1cXBxDhw6lb9++aLVapkyZYu/QXtu5c+eYNm0aYWFhFuMNo6KiuHDhgp2jE29LWpIysNq1a7N161YyZ85M5cqVOX78OFqtltq1a790vNK7ZO3atWzcuJFz585Rs2ZN2rRpQ7169dLdh3NG+cYcHBzM6tWrWb9+PUqlkhYtWjB8+HB7h5UilStX5siRIxbnkE6no1q1avzzzz92jCzl0utkgB49elh8UdNqtWg0GpKTk9HpdDg7O9sxupRp06YNuXPnxsPDg6CgIGrUqMHy5cvp3Llzun8Ei5CWpAzNYDCYx5E8yYWfXZceLFiwgI8++ohZs2al2+4dyBjfmMH0AMOhQ4fy+eefs2XLFlavXm3vkFKsSJEinDlzxvzbW2Ca7fnsc27Sg/Q8ff706dMWr598cVOr1eluJuu1a9dYuXIld+/eZdKkSXTr1o1y5coxYcIESZIygPR1NooUqVq1KhMmTGDs2LHmQZ3fffddupnZBrB79+5X/g5depAtWzYWLVpk/v+jR4+mu2/Mz3J0dKRNmza0adPG3qG8tnnz5gGQI0cO+vTpQ5s2bciVKxfh4eGsW7eORo0a2TnClEmvkwFsSc8dGu7u7mg0GnLnzs21a9cA0xjE4OBgO0cmUoMkSRnYiBEj6NevH5UqVUKv11OuXDny5cvHggUL7B3aK/Xu3ZtFixbRuXPnFyZJ6WF2yaZNm15ZplWrVmkeh4Bjx46Z/79YsWJcvHiRixcvAlCwYEFu3rxpr9DeyP379+nSpQvBwcGsX7+eEiVKMHnyZLp27ZpunjH0RHr+IlSgQAF++eUXPvnkE1xcXLh8+TKOjo7puk7iKUmSMjBPT0/WrFnD+fPnCQ4OxsfHh9KlS6eL8TxPukKqVKli50jezpOnOr+IQqGQJOlfsmLFCnuHkKrS62SAjObzzz+nX79+1KhRgx49etCuXTtUKhWffPKJvUMTqUCSpAzsxIkT5v/38vIiOTmZU6dO4eDgQNasWcmTJ48do3u5Pn36AKanhqe3n+541p49e2yuT0xMxMnJ6V+O5r9t27ZtNGvW7KWte+kpYS1VqhRjx45lzJgx5MuXj19++QWNRoOHh4e9Q3ul5ORki/dBp9NZvS/p4b0wGo14eXlx4MABHBwc+Pjjj7l//z7lypVLl92ewprMbsvAGjRoQEhICEqlkixZsvDgwQMMBoP5F88LFCjAwoUL3+kBq2XKlCFfvny0bduWFi1a4O7ubu+Q3khISAhffvklY8aMoUSJEkydOpUzZ84wd+5cvLy87B3ef0KzZs3Ytm0b9evXt7ldoVCkq5mG4eHhjB49mokTJxIYGGgxGeDJjym/q170HjyRHt6L+Ph4unfvjpeXl3m8W2RkJPXq1aNkyZIsWbIkXT3wVtgmSVIGNnv2bEJCQhg7diyurq7Ex8czZcoUcubMSefOnZk9ezaBgYHv9BilmJgYtm7dyqZNm7hy5Qrvvfcebdq0oVq1avYOLUX69OmDp6cnI0eOxM3NjaioKGbNmsWjR49e2SUnUo/BYODhw4fmh/wdOXKEgIAA6tSpk64eJDlv3jwuXrxIzZo16dixI0C6nD6fnn377becOXOG7777Dk9PT/P6yMhI+vXrR7Vq1eS32zICo8iw6tata4yPj7dYFx8fb6xTp47RaDQatVqtsVKlSnaI7M1cv37dOHPmTGOdOnWM7733nr3DSZFKlSoZk5KSLNZptVpj5cqV7RTRf8+9e/eMH3zwgXH48OFGo9Fo3LJli7FYsWLGDz/80FihQgXjuXPn7Bzh65k6daqxWrVqxoEDBxorV65sXLhwob1D+k9q2LCh8fbt2za3Xbp0ydioUaN/OSKRFpT2TtJE2omPjyc6OtpiXUxMDLGxsebX6WUGRnx8POfOneP8+fM8evSI0qVL2zukFFGr1URFRVmse/ToUbr7vbD0bNasWRQpUoSvvvoKgLlz59KrVy82bNjA2LFjmTt3rp0jfD3btm3j559/Zs6cOcyZM4etW7faO6T/pMjISPLmzWtzW7Fixbh///6/HJFICzJwOwNr3LgxAwYM4MsvvyRnzpyEhIQwZ84cGjVqRGxsLBMnTqRixYr2DvOl/v77bzZu3Mju3bvJlSsXbdq0YdasWWTOnNneoaVI48aNGTRoEIMHDyZHjhyEhoYyZ84c3n//fXuH9p9x+PBhNm/eTNasWQkJCSEwMJAWLVoApvF7EydOtHOErycmJobChQsDplmgYWFhdo7ov8nNzY0HDx6QJUsWq20PHz6Ubs8MQpKkDGzkyJFMmjSJAQMGkJCQgEajoU2bNgwZMoSLFy8SHR3N119/be8wX2rAgAE0bdqUH3/8kbJly9o7nDc2dOhQJkyYQJ8+fUhKSsLR0ZFWrVrJmIV/UWxsrHks0tmzZ3F3d6dgwYIAODk5odPp7Bnea1Mqn3YApLenU2ck1apVY9WqVXz22WdW21avXp2u71fiKbnCMjAnJyfzE7cfPnyIp6enuXutYsWK73wrEsAHH3zA8OHDcXNzs3cob+zZQbbjx48nOjra4r0Q/47MmTObf5n9+PHjlC9f3rzt5s2bNlsE3kVGmWvzTujTpw+tW7fmwYMHfPDBB3h7exMeHs7vv//O+vXrWblypb1DFKlAkqQM7ty5c9y6dcvqxpoenkECpp8lmTBhgr3DeGPTpk1j06ZNVKxYkTlz5hAXF0fv3r3tHdZ/Ur169fD396dhw4Zs3bqVcePGARAdHc3s2bOpVauWnSN8PRnlGUPpXf78+Vm6dCnjxo1j1apVKBQKjEYjfn5+LF68OF0/3008JY8AyMBmzpzJ4sWL8fb2tmiWTw/PIHli6tSpxMXF0bp1a7y9vS1aX3LmzGnHyF5P7dq1Wbp0KYULF+bYsWNMnDhRBtraSXR0NIMHD+bUqVM0bdqUSZMmAVCuXDm8vb1ZvXp1unhmVUZ4xlBGExQURFRUFN7e3univiRenyRJGVjdunUZP348derUsXcob6xo0aLm/3+SIBmNRhQKBZcvX7ZXWK+tXLly5l88T05Opnr16hw/ftzOUYlnHTp0iEqVKskT0IUQVqS7LQOLi4ujdu3a9g7jraT3b8QyyPbdV7NmTXuHIIR4R8ldOwOrW7cuW7duNU9zTo98fX3tHcJbkYZaIYRIvyRJysASExMZPnw4CxYssBprsXz5cjtFlTJFixZ94Syw9NDdJoNshRAi/ZIxSRnYkx9dtMXWsz3eRc+P34mKimLFihW0bNmSdu3a2Smq1yeDbIUQIv2SJEmkO/fv36dr165s377d3qEIIYTIwOS32zK43377jebNm1OlShVCQkIYNGgQcXFx9g7rrbi7u8tPMQghhEhzMiYpA/vpp5/45Zdf6NGjB9OmTcPV1ZWwsDCmTJmSbn6n6vnxOzqdjr/++otixYrZJyAhhBD/GdLdloG9//77zJ8/n4IFC1K5cmWOHz9OeHg4H374IYcPH7Z3eK/l+TE9KpWKggUL8tVXX1GoUCE7RSWEEOK/QFqSMrAHDx6QP39+4OlUdE9PT5KTk+0Z1mszGAysW7fO/KOkR44cISAggDp16lCgQAE7RyeEECKjkzFJGVjRokVZs2YN8PRp1Tt27KBw4cL2DOu1hIWF0bx5c6ZPnw7A1q1b6dGjB1u3bqVdu3acP3/ezhEKIYTI6KS7LQO7ePEiXbt2pWDBgly4cIFq1apx5swZlixZQpkyZewd3ksNHz6cpKQkRo0ahaenJ40aNaJJkyZ88cUXbNmyhW3btrFo0SJ7hymEECIDkyQpgwsLC2Pr1q0EBwfj4+ND8+bN08UPMNaqVYvNmzeTNWtWQkJCqF+/Ptu3b6dgwYLExcVRr149+Q00IYQQaUrGJGVwXl5e9OzZE6PRyIEDB4iIiEgXSVJsbKx5LNLZs2dxd3enYMGCADg5OaHT6ewZnhBCiP8AGZOUge3Zs4datWoB8MMPPzBw4EA6derEb7/9ZufIXi1z5sxERUUBpqduly9f3rzt5s2bZMmSxV6hCSGE+I+QJCkD++GHHxg8eDAGg4EVK1Ywd+5cVq1axeLFi+0d2ivVq1cPf39/duzYwdatW2natCkA0dHRzJ4925z8CSGEEGlFkqQMLDAwkHbt2hEQEIBWq6VGjRqULFmSiIgIe4f2Sl988QWPHj1i5MiRvP/++zRv3hyAOnXqcO3aNQYOHGjnCIUQQmR0MiYpA3N2diYyMpI9e/ZQoUIF1Go1AQEB6aKryt3dnWXLllmtnzt3LpUqVcLJyckOUQkhhPgvkSQpA/voo49o1aoV0dHRzJkzhwsXLtCzZ0+6d+9u79DeWM2aNe0dghBCiP8IeQRABnfs2DGcnJwoW7YsoaGhnD9/nkaNGtk7LCGEEOKdJy1JGVzBggXx8vIiKSmJvXv3pouuNiGEEOJdIElSBrZ27VomTZrEmTNnmD59Ojt27EChUHDr1i369+9v7/CEEEKId5rMbsvAVq5cyffff49er2fDhg3MnTuXX375JV08J0kIIYSwN2lJysBCQ0OpUaMGp06dQq1Wmx/IGB0dbefIhBBCiHeftCRlYJkzZ+bOnTvs3LmTypUrA3D06FG8vb3tHJkQQgjx7pOWpAysW7du5ocwrlixgpMnT9KnTx/GjRtn58iEEEKId588AiCDCwoKQq1WkyNHDqKioggJCaFkyZL2DksIIYR450l3Wwbn5eWFQqEgJCQErVZLpkyZ+PPPP+0dlhBCCPHOk5akDGz9+vX4+/uTmJhosd7T05NDhw7ZKSohhBAifZAxSRnYggULGDx4MK6urpw4cYIuXbowffp0atSoYe/QhBBCiHeedLdlYPfv36dLly5Uq1aNwMBASpQoweTJk1m7dq29QxNCCCHeeZIkZWCenp7odDpy5MjBrVu3AMiZMyeRkZF2jkwIIYR490mSlIGVKlWKsWPHotVqyZcvH7/88gsbN27Ew8PD3qEJIYQQ7zwZk5SBjRw5ktGjRxMXF8fQoUPp27cvWq2WKVOm2Ds0IYQQ4p0ns9syqHnz5nHx4kVq1qxJx44dAUhOTkan0+Hs7Gzn6IQQQoh3n3S3ZUDTpk1j9erVODg4MGfOHBYtWgSAWq2WBEkIIYR4TdKSlAHVrl2bpUuXUrhwYY4dO8bEiRPZunWrvcMSQggh0hVpScqAYmJiKFy4MAAVKlQgLCzMzhEJIYQQ6Y8kSRmQUvn0bVWrZWy+EEII8SYkScqApAdVCCGEeHvSzJABJScns2nTJvNrnU5n8RqgVatW/2pMQgghRHojA7czoPr16790u0Kh4K+//vqXohFCCCHSJ0mShBBCCCFskDFJQgghhBA2SJIkhBBCCGGDJElCCCGEEDZIkiSEEEIIYYMkSUIIIYQQNkiSJIQQQghhgyRJQgghhBA2/B+xRkmxQqVuLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot = True , cmap = 'rocket')\n",
    "plt.title('Correlation using Heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444cbeab",
   "metadata": {},
   "source": [
    "From this correlation matrix, we can say that age and Pclass are highly negatively correlated and Cabin and age are little positively correlated. This is because of the fact that, as previously visualized, age of 2nd, 3rd class passenger tends to have lower age. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3b757e",
   "metadata": {},
   "source": [
    "## Converting Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ab642cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7f476a5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare  Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500      0        S  \n",
       "1      0          PC 17599  71.2833      1        C  \n",
       "2      0  STON/O2. 3101282   7.9250      0        S  \n",
       "3      0            113803  53.1000      1        S  \n",
       "4      0            373450   8.0500      0        S  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3168ddc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "25c36761",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = ColumnTransformer( [\n",
    "    ('drop_cols' , 'drop' , ['PassengerId' , \"Name\" , \"Ticket\"]),\n",
    "    ('oneHotEncoder' , OneHotEncoder(sparse = False , handle_unknown = 'ignore' , drop = 'first') , ['Sex' ,'Embarked'])\n",
    "] , remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4e6b9b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.    ,  0.    ,  1.    , ...,  0.    ,  7.25  ,  0.    ],\n",
       "       [ 0.    ,  0.    ,  0.    , ...,  0.    , 71.2833,  1.    ],\n",
       "       [ 0.    ,  0.    ,  1.    , ...,  0.    ,  7.925 ,  0.    ],\n",
       "       ...,\n",
       "       [ 0.    ,  0.    ,  1.    , ...,  2.    , 23.45  ,  0.    ],\n",
       "       [ 1.    ,  0.    ,  0.    , ...,  0.    , 30.    ,  1.    ],\n",
       "       [ 1.    ,  1.    ,  0.    , ...,  0.    ,  7.75  ,  0.    ]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a1d1472",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1fc81af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5699df94",
   "metadata": {},
   "source": [
    "Drop the columns and apply Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "220128ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = ColumnTransformer([\n",
    "    \n",
    "    ('scaler' , StandardScaler(), slice(0,df.shape[1]))\n",
    "] , remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "db129ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7fc601f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "lg = LogisticRegression(max_iter = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec8a304a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5adccc35",
   "metadata": {},
   "source": [
    "## Making pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e8c9633c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('ohe', ohe),\n",
    "    ('scale', scale ),\n",
    "    ('model' , lg)\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ebe935",
   "metadata": {},
   "source": [
    "## Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "dcf6aa0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(df.drop(['Survived'] , axis = 1) , df['Survived'] , test_size = 0.3 , random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3beec20",
   "metadata": {},
   "source": [
    "## Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1e4d36d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display = 'diagram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7a10dc16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 {color: black;background-color: white;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 pre{padding: 0;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-toggleable {background-color: white;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-estimator:hover {background-color: #d4ebff;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-item {z-index: 1;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-parallel-item:only-child::after {width: 0;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-096adfb8-5ce0-46f4-98f9-ebb3ac199169\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ohe&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;drop_cols&#x27;, &#x27;drop&#x27;,\n",
       "                                                  [&#x27;PassengerId&#x27;, &#x27;Name&#x27;,\n",
       "                                                   &#x27;Ticket&#x27;]),\n",
       "                                                 (&#x27;oneHotEncoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Embarked&#x27;])])),\n",
       "                (&#x27;scale&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n",
       "                                                  slice(0, 12, None))])),\n",
       "                (&#x27;model&#x27;, LogisticRegression(max_iter=10000))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5f066b6a-b4b8-4680-8acd-200ec3b9bbcb\" type=\"checkbox\" ><label for=\"5f066b6a-b4b8-4680-8acd-200ec3b9bbcb\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ohe&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;drop_cols&#x27;, &#x27;drop&#x27;,\n",
       "                                                  [&#x27;PassengerId&#x27;, &#x27;Name&#x27;,\n",
       "                                                   &#x27;Ticket&#x27;]),\n",
       "                                                 (&#x27;oneHotEncoder&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse=False),\n",
       "                                                  [&#x27;Sex&#x27;, &#x27;Embarked&#x27;])])),\n",
       "                (&#x27;scale&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n",
       "                                                  slice(0, 12, None))])),\n",
       "                (&#x27;model&#x27;, LogisticRegression(max_iter=10000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"29e14d84-13e8-420e-a8b1-982a68329eeb\" type=\"checkbox\" ><label for=\"29e14d84-13e8-420e-a8b1-982a68329eeb\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ohe: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;drop_cols&#x27;, &#x27;drop&#x27;,\n",
       "                                 [&#x27;PassengerId&#x27;, &#x27;Name&#x27;, &#x27;Ticket&#x27;]),\n",
       "                                (&#x27;oneHotEncoder&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               handle_unknown=&#x27;ignore&#x27;,\n",
       "                                               sparse=False),\n",
       "                                 [&#x27;Sex&#x27;, &#x27;Embarked&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"66c8d506-6f3a-4d71-b0bf-5912848695f7\" type=\"checkbox\" ><label for=\"66c8d506-6f3a-4d71-b0bf-5912848695f7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop_cols</label><div class=\"sk-toggleable__content\"><pre>[&#x27;PassengerId&#x27;, &#x27;Name&#x27;, &#x27;Ticket&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"92b502cf-d71c-46ad-898d-fcf623d13687\" type=\"checkbox\" ><label for=\"92b502cf-d71c-46ad-898d-fcf623d13687\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">drop</label><div class=\"sk-toggleable__content\"><pre>drop</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"523fc973-de2a-44c2-adb1-657d40d26809\" type=\"checkbox\" ><label for=\"523fc973-de2a-44c2-adb1-657d40d26809\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">oneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Sex&#x27;, &#x27;Embarked&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2a31e213-8273-42d2-80cf-87b0c0564ac0\" type=\"checkbox\" ><label for=\"2a31e213-8273-42d2-80cf-87b0c0564ac0\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"826e5ce1-704f-461c-9132-a3f3b97097b1\" type=\"checkbox\" ><label for=\"826e5ce1-704f-461c-9132-a3f3b97097b1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Pclass&#x27;, &#x27;Age&#x27;, &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Fare&#x27;, &#x27;Cabin&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ea93de27-53bb-4cb0-bac3-32131f96a3dd\" type=\"checkbox\" ><label for=\"ea93de27-53bb-4cb0-bac3-32131f96a3dd\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7e7d8444-3f08-4f6c-b572-a73576c80284\" type=\"checkbox\" ><label for=\"7e7d8444-3f08-4f6c-b572-a73576c80284\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scale: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;scaler&#x27;, StandardScaler(),\n",
       "                                 slice(0, 12, None))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f529429e-fd87-4433-af87-fedaccb1b399\" type=\"checkbox\" ><label for=\"f529429e-fd87-4433-af87-fedaccb1b399\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">scaler</label><div class=\"sk-toggleable__content\"><pre>slice(0, 12, None)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b5990686-d4d4-4de6-b6ab-e1d9d0d4c5dd\" type=\"checkbox\" ><label for=\"b5990686-d4d4-4de6-b6ab-e1d9d0d4c5dd\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a6c4c8c4-3e11-4472-ac5c-ac5be4a06af5\" type=\"checkbox\" ><label for=\"a6c4c8c4-3e11-4472-ac5c-ac5be4a06af5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"522383ae-a847-4054-9e56-4536574ef0c6\" type=\"checkbox\" ><label for=\"522383ae-a847-4054-9e56-4536574ef0c6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a1513bbc-c495-40d6-87c4-9af143b79115\" type=\"checkbox\" ><label for=\"a1513bbc-c495-40d6-87c4-9af143b79115\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=10000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ohe',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('drop_cols', 'drop',\n",
       "                                                  ['PassengerId', 'Name',\n",
       "                                                   'Ticket']),\n",
       "                                                 ('oneHotEncoder',\n",
       "                                                  OneHotEncoder(drop='first',\n",
       "                                                                handle_unknown='ignore',\n",
       "                                                                sparse=False),\n",
       "                                                  ['Sex', 'Embarked'])])),\n",
       "                ('scale',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('scaler', StandardScaler(),\n",
       "                                                  slice(0, 12, None))])),\n",
       "                ('model', LogisticRegression(max_iter=10000))])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f1b2549",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "77aed0ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipe.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9d13617c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "692b5c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[138,  19],\n",
       "       [ 28,  83]], dtype=int64)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "54f88c4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "26b1c953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8246268656716418"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(ytest, predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3647362a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "64fd2323",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(pipe, open('pipe.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eb94253",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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